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Research Recommendations – Examples and Writing Guide

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Research Recommendations

Definition:

Research recommendations refer to suggestions or advice given to someone who is looking to conduct research on a specific topic or area. These recommendations may include suggestions for research methods, data collection techniques, sources of information, and other factors that can help to ensure that the research is conducted in a rigorous and effective manner. Research recommendations may be provided by experts in the field, such as professors, researchers, or consultants, and are intended to help guide the researcher towards the most appropriate and effective approach to their research project.

Parts of Research Recommendations

Research recommendations can vary depending on the specific project or area of research, but typically they will include some or all of the following parts:

• Research question or objective : This is the overarching goal or purpose of the research project.
• Research methods : This includes the specific techniques and strategies that will be used to collect and analyze data. The methods will depend on the research question and the type of data being collected.
• Data collection: This refers to the process of gathering information or data that will be used to answer the research question. This can involve a range of different methods, including surveys, interviews, observations, or experiments.
• Data analysis : This involves the process of examining and interpreting the data that has been collected. This can involve statistical analysis, qualitative analysis, or a combination of both.
• Results and conclusions: This section summarizes the findings of the research and presents any conclusions or recommendations based on those findings.
• Limitations and future research: This section discusses any limitations of the study and suggests areas for future research that could build on the findings of the current project.

How to Write Research Recommendations

Writing research recommendations involves providing specific suggestions or advice to a researcher on how to conduct their study. Here are some steps to consider when writing research recommendations:

• Understand the research question: Before writing research recommendations, it is important to have a clear understanding of the research question and the objectives of the study. This will help to ensure that the recommendations are relevant and appropriate.
• Consider the research methods: Consider the most appropriate research methods that could be used to collect and analyze data that will address the research question. Identify the strengths and weaknesses of the different methods and how they might apply to the specific research question.
• Provide specific recommendations: Provide specific and actionable recommendations that the researcher can implement in their study. This can include recommendations related to sample size, data collection techniques, research instruments, data analysis methods, or other relevant factors.
• Justify recommendations : Justify why each recommendation is being made and how it will help to address the research question or objective. It is important to provide a clear rationale for each recommendation to help the researcher understand why it is important.
• Consider limitations and ethical considerations : Consider any limitations or potential ethical considerations that may arise in conducting the research. Provide recommendations for addressing these issues or mitigating their impact.
• Summarize recommendations: Provide a summary of the recommendations at the end of the report or document, highlighting the most important points and emphasizing how the recommendations will contribute to the overall success of the research project.

Example of Research Recommendations

Example of Research Recommendations sample for students:

• Further investigate the effects of X on Y by conducting a larger-scale randomized controlled trial with a diverse population.
• Explore the relationship between A and B by conducting qualitative interviews with individuals who have experience with both.
• Investigate the long-term effects of intervention C by conducting a follow-up study with participants one year after completion.
• Examine the effectiveness of intervention D in a real-world setting by conducting a field study in a naturalistic environment.
• Compare and contrast the results of this study with those of previous research on the same topic to identify any discrepancies or inconsistencies in the findings.
• Expand upon the limitations of this study by addressing potential confounding variables and conducting further analyses to control for them.
• Investigate the relationship between E and F by conducting a meta-analysis of existing literature on the topic.
• Explore the potential moderating effects of variable G on the relationship between H and I by conducting subgroup analyses.
• Identify potential areas for future research based on the gaps in current literature and the findings of this study.
• Conduct a replication study to validate the results of this study and further establish the generalizability of the findings.

Applications of Research Recommendations

Research recommendations are important as they provide guidance on how to improve or solve a problem. The applications of research recommendations are numerous and can be used in various fields. Some of the applications of research recommendations include:

• Policy-making: Research recommendations can be used to develop policies that address specific issues. For example, recommendations from research on climate change can be used to develop policies that reduce carbon emissions and promote sustainability.
• Program development: Research recommendations can guide the development of programs that address specific issues. For example, recommendations from research on education can be used to develop programs that improve student achievement.
• Product development : Research recommendations can guide the development of products that meet specific needs. For example, recommendations from research on consumer behavior can be used to develop products that appeal to consumers.
• Marketing strategies: Research recommendations can be used to develop effective marketing strategies. For example, recommendations from research on target audiences can be used to develop marketing strategies that effectively reach specific demographic groups.
• Medical practice : Research recommendations can guide medical practitioners in providing the best possible care to patients. For example, recommendations from research on treatments for specific conditions can be used to improve patient outcomes.
• Scientific research: Research recommendations can guide future research in a specific field. For example, recommendations from research on a specific disease can be used to guide future research on treatments and cures for that disease.

Purpose of Research Recommendations

The purpose of research recommendations is to provide guidance on how to improve or solve a problem based on the findings of research. Research recommendations are typically made at the end of a research study and are based on the conclusions drawn from the research data. The purpose of research recommendations is to provide actionable advice to individuals or organizations that can help them make informed decisions, develop effective strategies, or implement changes that address the issues identified in the research.

The main purpose of research recommendations is to facilitate the transfer of knowledge from researchers to practitioners, policymakers, or other stakeholders who can benefit from the research findings. Recommendations can help bridge the gap between research and practice by providing specific actions that can be taken based on the research results. By providing clear and actionable recommendations, researchers can help ensure that their findings are put into practice, leading to improvements in various fields, such as healthcare, education, business, and public policy.

Characteristics of Research Recommendations

Research recommendations are a key component of research studies and are intended to provide practical guidance on how to apply research findings to real-world problems. The following are some of the key characteristics of research recommendations:

• Actionable : Research recommendations should be specific and actionable, providing clear guidance on what actions should be taken to address the problem identified in the research.
• Evidence-based: Research recommendations should be based on the findings of the research study, supported by the data collected and analyzed.
• Contextual: Research recommendations should be tailored to the specific context in which they will be implemented, taking into account the unique circumstances and constraints of the situation.
• Feasible : Research recommendations should be realistic and feasible, taking into account the available resources, time constraints, and other factors that may impact their implementation.
• Prioritized: Research recommendations should be prioritized based on their potential impact and feasibility, with the most important recommendations given the highest priority.
• Communicated effectively: Research recommendations should be communicated clearly and effectively, using language that is understandable to the target audience.
• Evaluated : Research recommendations should be evaluated to determine their effectiveness in addressing the problem identified in the research, and to identify opportunities for improvement.

Advantages of Research Recommendations

Research recommendations have several advantages, including:

• Providing practical guidance: Research recommendations provide practical guidance on how to apply research findings to real-world problems, helping to bridge the gap between research and practice.
• Improving decision-making: Research recommendations help decision-makers make informed decisions based on the findings of research, leading to better outcomes and improved performance.
• Enhancing accountability : Research recommendations can help enhance accountability by providing clear guidance on what actions should be taken, and by providing a basis for evaluating progress and outcomes.
• Informing policy development : Research recommendations can inform the development of policies that are evidence-based and tailored to the specific needs of a given situation.
• Enhancing knowledge transfer: Research recommendations help facilitate the transfer of knowledge from researchers to practitioners, policymakers, or other stakeholders who can benefit from the research findings.
• Encouraging further research : Research recommendations can help identify gaps in knowledge and areas for further research, encouraging continued exploration and discovery.
• Promoting innovation: Research recommendations can help identify innovative solutions to complex problems, leading to new ideas and approaches.

Limitations of Research Recommendations

While research recommendations have several advantages, there are also some limitations to consider. These limitations include:

• Context-specific: Research recommendations may be context-specific and may not be applicable in all situations. Recommendations developed in one context may not be suitable for another context, requiring adaptation or modification.
• I mplementation challenges: Implementation of research recommendations may face challenges, such as lack of resources, resistance to change, or lack of buy-in from stakeholders.
• Limited scope: Research recommendations may be limited in scope, focusing only on a specific issue or aspect of a problem, while other important factors may be overlooked.
• Uncertainty : Research recommendations may be uncertain, particularly when the research findings are inconclusive or when the recommendations are based on limited data.
• Bias : Research recommendations may be influenced by researcher bias or conflicts of interest, leading to recommendations that are not in the best interests of stakeholders.
• Timing : Research recommendations may be time-sensitive, requiring timely action to be effective. Delayed action may result in missed opportunities or reduced effectiveness.
• Lack of evaluation: Research recommendations may not be evaluated to determine their effectiveness or impact, making it difficult to assess whether they are successful or not.

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Research Recommendations – Guiding policy-makers for evidence-based decision making

Research recommendations play a crucial role in guiding scholars and researchers toward fruitful avenues of exploration. In an era marked by rapid technological advancements and an ever-expanding knowledge base, refining the process of generating research recommendations becomes imperative.

But, what is a research recommendation?

Research recommendations are suggestions or advice provided to researchers to guide their study on a specific topic . They are typically given by experts in the field. Research recommendations are more action-oriented and provide specific guidance for decision-makers, unlike implications that are broader and focus on the broader significance and consequences of the research findings. However, both are crucial components of a research study.

Difference Between Research Recommendations and Implication

Although research recommendations and implications are distinct components of a research study, they are closely related. The differences between them are as follows:

Types of Research Recommendations

Recommendations in research can take various forms, which are as follows:

These recommendations aim to assist researchers in navigating the vast landscape of academic knowledge.

Let us dive deeper to know about its key components and the steps to write an impactful research recommendation.

Key Components of Research Recommendations

The key components of research recommendations include defining the research question or objective, specifying research methods, outlining data collection and analysis processes, presenting results and conclusions, addressing limitations, and suggesting areas for future research. Here are some characteristics of research recommendations:

Research recommendations offer various advantages and play a crucial role in ensuring that research findings contribute to positive outcomes in various fields. However, they also have few limitations which highlights the significance of a well-crafted research recommendation in offering the promised advantages.

The importance of research recommendations ranges in various fields, influencing policy-making, program development, product development, marketing strategies, medical practice, and scientific research. Their purpose is to transfer knowledge from researchers to practitioners, policymakers, or stakeholders, facilitating informed decision-making and improving outcomes in different domains.

How to Write Research Recommendations?

Research recommendations can be generated through various means, including algorithmic approaches, expert opinions, or collaborative filtering techniques. Here is a step-wise guide to build your understanding on the development of research recommendations.

1. Understand the Research Question:

Understand the research question and objectives before writing recommendations. Also, ensure that your recommendations are relevant and directly address the goals of the study.

2. Review Existing Literature:

Familiarize yourself with relevant existing literature to help you identify gaps , and offer informed recommendations that contribute to the existing body of research.

3. Consider Research Methods:

Evaluate the appropriateness of different research methods in addressing the research question. Also, consider the nature of the data, the study design, and the specific objectives.

4. Identify Data Collection Techniques:

Gather dataset from diverse authentic sources. Include information such as keywords, abstracts, authors, publication dates, and citation metrics to provide a rich foundation for analysis.

5. Propose Data Analysis Methods:

Suggest appropriate data analysis methods based on the type of data collected. Consider whether statistical analysis, qualitative analysis, or a mixed-methods approach is most suitable.

6. Consider Limitations and Ethical Considerations:

Acknowledge any limitations and potential ethical considerations of the study. Furthermore, address these limitations or mitigate ethical concerns to ensure responsible research.

7. Justify Recommendations:

Explain how your recommendation contributes to addressing the research question or objective. Provide a strong rationale to help researchers understand the importance of following your suggestions.

8. Summarize Recommendations:

Provide a concise summary at the end of the report to emphasize how following these recommendations will contribute to the overall success of the research project.

By following these steps, you can create research recommendations that are actionable and contribute meaningfully to the success of the research project.

Download now to unlock some tips to improve your journey of writing research recommendations.

Example of a Research Recommendation

Here is an example of a research recommendation based on a hypothetical research to improve your understanding.

Research Recommendation: Enhancing Student Learning through Integrated Learning Platforms

Background:

The research study investigated the impact of an integrated learning platform on student learning outcomes in high school mathematics classes. The findings revealed a statistically significant improvement in student performance and engagement when compared to traditional teaching methods.

Recommendation:

In light of the research findings, it is recommended that educational institutions consider adopting and integrating the identified learning platform into their mathematics curriculum. The following specific recommendations are provided:

• Implementation of the Integrated Learning Platform:

Schools are encouraged to adopt the integrated learning platform in mathematics classrooms, ensuring proper training for teachers on its effective utilization.

• Professional Development for Educators:

Develop and implement professional programs to train educators in the effective use of the integrated learning platform to address any challenges teachers may face during the transition.

• Monitoring and Evaluation:

Establish a monitoring and evaluation system to track the impact of the integrated learning platform on student performance over time.

• Resource Allocation:

Allocate sufficient resources, both financial and technical, to support the widespread implementation of the integrated learning platform.

By implementing these recommendations, educational institutions can harness the potential of the integrated learning platform and enhance student learning experiences and academic achievements in mathematics.

This example covers the components of a research recommendation, providing specific actions based on the research findings, identifying the target audience, and outlining practical steps for implementation.

Using AI in Research Recommendation Writing

Enhancing research recommendations is an ongoing endeavor that requires the integration of cutting-edge technologies, collaborative efforts, and ethical considerations. By embracing data-driven approaches and leveraging advanced technologies, the research community can create more effective and personalized recommendation systems. However, it is accompanied by several limitations. Therefore, it is essential to approach the use of AI in research with a critical mindset, and complement its capabilities with human expertise and judgment.

Here are some limitations of integrating AI in writing research recommendation and some ways on how to counter them.

1. Data Bias

AI systems rely heavily on data for training. If the training data is biased or incomplete, the AI model may produce biased results or recommendations.

How to tackle: Audit regularly the model’s performance to identify any discrepancies and adjust the training data and algorithms accordingly.

2. Lack of Understanding of Context:

AI models may struggle to understand the nuanced context of a particular research problem. They may misinterpret information, leading to inaccurate recommendations.

How to tackle: Use AI to characterize research articles and topics. Employ them to extract features like keywords, authorship patterns and content-based details.

3. Ethical Considerations:

AI models might stereotype certain concepts or generate recommendations that could have negative consequences for certain individuals or groups.

How to tackle: Incorporate user feedback mechanisms to reduce redundancies. Establish an ethics review process for AI models in research recommendation writing.

4. Lack of Creativity and Intuition:

AI may struggle with tasks that require a deep understanding of the underlying principles or the ability to think outside the box.

How to tackle: Hybrid approaches can be employed by integrating AI in data analysis and identifying patterns for accelerating the data interpretation process.

5. Interpretability:

Many AI models, especially complex deep learning models, lack transparency on how the model arrived at a particular recommendation.

How to tackle: Implement models like decision trees or linear models. Provide clear explanation of the model architecture, training process, and decision-making criteria.

6. Dynamic Nature of Research:

Research fields are dynamic, and new information is constantly emerging. AI models may struggle to keep up with the rapidly changing landscape and may not be able to adapt to new developments.

How to tackle: Establish a feedback loop for continuous improvement. Regularly update the recommendation system based on user feedback and emerging research trends.

The integration of AI in research recommendation writing holds great promise for advancing knowledge and streamlining the research process. However, navigating these concerns is pivotal in ensuring the responsible deployment of these technologies. Researchers need to understand the use of responsible use of AI in research and must be aware of the ethical considerations.

Exploring research recommendations plays a critical role in shaping the trajectory of scientific inquiry. It serves as a compass, guiding researchers toward more robust methodologies, collaborative endeavors, and innovative approaches. Embracing these suggestions not only enhances the quality of individual studies but also contributes to the collective advancement of human understanding.

Frequently Asked Questions

The purpose of recommendations in research is to provide practical and actionable suggestions based on the study's findings, guiding future actions, policies, or interventions in a specific field or context. Recommendations bridges the gap between research outcomes and their real-world application.

To make a research recommendation, analyze your findings, identify key insights, and propose specific, evidence-based actions. Include the relevance of the recommendations to the study's objectives and provide practical steps for implementation.

Begin a recommendation by succinctly summarizing the key findings of the research. Clearly state the purpose of the recommendation and its intended impact. Use a direct and actionable language to convey the suggested course of action.

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Research Implications & Recommendations

A Plain-Language Explainer With Examples + FREE Template

By: Derek Jansen (MBA) | Reviewers: Dr Eunice Rautenbach | May 2024

The research implications and recommendations are closely related but distinctly different concepts that often trip students up. Here, we’ll unpack them using plain language and loads of examples , so that you can approach your project with confidence.

Overview: Implications & Recommendations

• What are research implications ?
• What are research recommendations ?
• Examples of implications and recommendations
• The “ Big 3 ” categories
• How to write the implications and recommendations
• Template sentences for both sections
• Key takeaways

Implications & Recommendations 101

Let’s start with the basics and define our terms.

At the simplest level, research implications refer to the possible effects or outcomes of a study’s findings. More specifically, they answer the question, “ What do these findings mean?” . In other words, the implications section is where you discuss the broader impact of your study’s findings on theory, practice and future research.

This discussion leads us to the recommendations section , which is where you’ll propose specific actions based on your study’s findings and answer the question, “ What should be done next?” . In other words, the recommendations are practical steps that stakeholders can take to address the key issues identified by your study.

In a nutshell, then, the research implications discuss the broader impact and significance of a study’s findings, while recommendations provide specific actions to take, based on those findings. So, while both of these components are deeply rooted in the findings of the study, they serve different functions within the write up.

Need a helping hand?

Examples: Implications & Recommendations

The distinction between research implications and research recommendations might still feel a bit conceptual, so let’s look at one or two practical examples:

Let’s assume that your study finds that interactive learning methods significantly improve student engagement compared to traditional lectures. In this case, one of your recommendations could be that schools incorporate more interactive learning techniques into their curriculums to enhance student engagement.

Let’s imagine that your study finds that patients who receive personalised care plans have better health outcomes than those with standard care plans. One of your recommendations might be that healthcare providers develop and implement personalised care plans for their patients.

Now, these are admittedly quite simplistic examples, but they demonstrate the difference (and connection ) between the research implications and the recommendations. Simply put, the implications are about the impact of the findings, while the recommendations are about proposed actions, based on the findings.

The “Big 3” Categories

Now that we’ve defined our terms, let’s dig a little deeper into the implications – specifically, the different types or categories of research implications that exist.

Broadly speaking, implications can be divided into three categories – theoretical implications, practical implications and implications for future research .

Theoretical implications relate to how your study’s findings contribute to or challenge existing theories. For example, if a study on social behaviour uncovers new patterns, it might suggest that modifications to current psychological theories are necessary.

Practical implications , on the other hand, focus on how your study’s findings can be applied in real-world settings. For example, if your study demonstrated the effectiveness of a new teaching method, this would imply that educators should consider adopting this method to improve learning outcomes.

Practical implications can also involve policy reconsiderations . For example, if a study reveals significant health benefits from a particular diet, an implication might be that public health guidelines be re-evaluated.

Last but not least, there are the implications for future research . As the name suggests, this category of implications highlights the research gaps or new questions raised by your study. For example, if your study finds mixed results regarding a relationship between two variables, it might imply the need for further investigation to clarify these findings.

To recap then, the three types of implications are the theoretical, the practical and the implications on future research. Regardless of the category, these implications feed into and shape the recommendations , laying the foundation for the actions you’ll propose.

How To Write The  Sections

Now that we’ve laid the foundations, it’s time to explore how to write up the implications and recommendations sections respectively.

Let’s start with the “ where ” before digging into the “ how ”. Typically, the implications will feature in the discussion section of your document, while the recommendations will be located in the conclusion . That said, layouts can vary between disciplines and institutions, so be sure to check with your university what their preferences are.

For the implications section, a common approach is to structure the write-up based on the three categories we looked at earlier – theoretical, practical and future research implications. In practical terms, this discussion will usually follow a fairly formulaic sentence structure – for example:

This research provides new insights into [theoretical aspect], indicating that…

The study’s outcomes highlight the potential benefits of adopting [specific practice] in..

This study raises several questions that warrant further investigation, such as…

Moving onto the recommendations section, you could again structure your recommendations using the three categories. Alternatively, you could structure the discussion per stakeholder group – for example, policymakers, organisations, researchers, etc.

Again, you’ll likely use a fairly formulaic sentence structure for this section. Here are some examples for your inspiration: 

Based on the findings, [specific group] should consider adopting [new method] to improve…

To address the issues identified, it is recommended that legislation should be introduced to…

Researchers should consider examining [specific variable] to build on the current study’s findings.

Remember, you can grab a copy of our tried and tested templates for both the discussion and conclusion sections over on the Grad Coach blog. You can find the links to those, as well as loads of other free resources, in the description 🙂

FAQs: Implications & Recommendations

How do i determine the implications of my study.

To do this, you’ll need to consider how your findings address gaps in the existing literature, how they could influence theory, practice, or policy, and the potential societal or economic impacts.

When thinking about your findings, it’s also a good idea to revisit your introduction chapter, where you would have discussed the potential significance of your study more broadly. This section can help spark some additional ideas about what your findings mean in relation to your original research aims. 

Should I discuss both positive and negative implications?

Absolutely. You’ll need to discuss both the positive and negative implications to provide a balanced view of how your findings affect the field and any limitations or potential downsides.

Can my research implications be speculative?

Yes and no. While implications are somewhat more speculative than recommendations and can suggest potential future outcomes, they should be grounded in your data and analysis. So, be careful to avoid overly speculative claims.

How do I formulate recommendations?

Ideally, you should base your recommendations on the limitations and implications of your study’s findings. So, consider what further research is needed, how policies could be adapted, or how practices could be improved – and make proposals in this respect.

How specific should my recommendations be?

Your recommendations should be as specific as possible, providing clear guidance on what actions or research should be taken next. As mentioned earlier, the implications can be relatively broad, but the recommendations should be very specific and actionable. Ideally, you should apply the SMART framework to your recommendations.

Can I recommend future research in my recommendations?

Absolutely. Highlighting areas where further research is needed is a key aspect of the recommendations section. Naturally, these recommendations should link to the respective section of your implications (i.e., implications for future research).

Wrapping Up: Key Takeaways

We’ve covered quite a bit of ground here, so let’s quickly recap.

• Research implications refer to the possible effects or outcomes of a study’s findings.
• The recommendations section, on the other hand, is where you’ll propose specific actions based on those findings.
• You can structure your implications section based on the three overarching categories – theoretical, practical and future research implications.
• You can carry this structure through to the recommendations as well, or you can group your recommendations by stakeholder.

Remember to grab a copy of our tried and tested free dissertation template, which covers both the implications and recommendations sections. If you’d like 1:1 help with your research project, be sure to check out our private coaching service, where we hold your hand throughout the research journey, step by step.

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• How to Write Recommendations in Research | Examples & Tips

How to Write Recommendations in Research | Examples & Tips

Published on 15 September 2022 by Tegan George .

Recommendations in research are a crucial component of your discussion section and the conclusion of your thesis , dissertation , or research paper .

As you conduct your research and analyse the data you collected , perhaps there are ideas or results that don’t quite fit the scope of your research topic . Or, maybe your results suggest that there are further implications of your results or the causal relationships between previously-studied variables than covered in extant research.

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Table of contents

What should recommendations look like, building your research recommendation, how should your recommendations be written, recommendation in research example, frequently asked questions about recommendations.

Recommendations for future research should be:

• Concrete and specific
• Supported with a clear rationale
• Directly connected to your research

Overall, strive to highlight ways other researchers can reproduce or replicate your results to draw further conclusions, and suggest different directions that future research can take, if applicable.

Relatedly, when making these recommendations, avoid:

• Undermining your own work, but rather offer suggestions on how future studies can build upon it
• Suggesting recommendations actually needed to complete your argument, but rather ensure that your research stands alone on its own merits
• Using recommendations as a place for self-criticism, but rather as a natural extension point for your work

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There are many different ways to frame recommendations, but the easiest is perhaps to follow the formula of research question   conclusion  recommendation. Here’s an example.

Conclusion An important condition for controlling many social skills is mastering language. If children have a better command of language, they can express themselves better and are better able to understand their peers. Opportunities to practice social skills are thus dependent on the development of language skills.

As a rule of thumb, try to limit yourself to only the most relevant future recommendations: ones that stem directly from your work. While you can have multiple recommendations for each research conclusion, it is also acceptable to have one recommendation that is connected to more than one conclusion.

These recommendations should be targeted at your audience, specifically toward peers or colleagues in your field that work on similar topics to yours. They can flow directly from any limitations you found while conducting your work, offering concrete and actionable possibilities for how future research can build on anything that your own work was unable to address at the time of your writing.

See below for a full research recommendation example that you can use as a template to write your own.

The current study can be interpreted as a first step in the research on COPD speech characteristics. However, the results of this study should be treated with caution due to the small sample size and the lack of details regarding the participants’ characteristics.

Future research could further examine the differences in speech characteristics between exacerbated COPD patients, stable COPD patients, and healthy controls. It could also contribute to a deeper understanding of the acoustic measurements suitable for e-health measurements.

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While it may be tempting to present new arguments or evidence in your thesis or disseration conclusion , especially if you have a particularly striking argument you’d like to finish your analysis with, you shouldn’t. Theses and dissertations follow a more formal structure than this.

All your findings and arguments should be presented in the body of the text (more specifically in the discussion section and results section .) The conclusion is meant to summarize and reflect on the evidence and arguments you have already presented, not introduce new ones.

The conclusion of your thesis or dissertation should include the following:

• A restatement of your research question
• A summary of your key arguments and/or results
• A short discussion of the implications of your research

For a stronger dissertation conclusion , avoid including:

• Generic concluding phrases (e.g. “In conclusion…”)
• Weak statements that undermine your argument (e.g. “There are good points on both sides of this issue.”)

Your conclusion should leave the reader with a strong, decisive impression of your work.

In a thesis or dissertation, the discussion is an in-depth exploration of the results, going into detail about the meaning of your findings and citing relevant sources to put them in context.

The conclusion is more shorter and more general: it concisely answers your main research question and makes recommendations based on your overall findings.

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• Mike Clarke , director 4 ,
• Mark Fenton , editor 3 ,
• Carol Forbes , reviews manager 5 ,
• Julie Glanville , associate director/information service manager 5 ,
• Nicholas J Hicks , consultant in public health medicine 6 ,
• Janet Moody , identification and prioritisation manager 6 ,
• Sara Twaddle , director 7 ,
• Hazim Timimi , systems developer 8 ,
• Pamela Young , senior programme manager 6
• 1 BMJ Publishing Group, London WC1H 9JR,
• 2 National Institute for Health and Clinical Excellence, London WC1V 6NA,
• 3 Database of Uncertainties about the Effects of Treatments, James Lind Alliance Secretariat, James Lind Initiative, Oxford OX2 7LG,
• 4 UK Cochrane Centre, Oxford OX2 7LG,
• 5 Centre for Reviews and Dissemination, University of York, York YO10 5DD,
• 6 National Coordinating Centre for Health Technology Assessment, University of Southampton, Southampton SO16 7PX,
• 7 Scottish Intercollegiate Guidelines Network, Edinburgh EH2 1EN,
• 8 Update Software, Oxford OX2 7LG
• Correspondence to: PBrown
• Accepted 22 September 2006

“More research is needed” is a conclusion that fits most systematic reviews. But authors need to be more specific about what exactly is required

Long awaited reports of new research, systematic reviews, and clinical guidelines are too often a disappointing anticlimax for those wishing to use them to direct future research. After many months or years of effort and intellectual energy put into these projects, authors miss the opportunity to identify unanswered questions and outstanding gaps in the evidence. Most reports contain only a less than helpful, general research recommendation. This means that the potential value of these recommendations is lost.

Current recommendations

In 2005, representatives of organisations commissioning and summarising research, including the BMJ Publishing Group, the Centre for Reviews and Dissemination, the National Coordinating Centre for Health Technology Assessment, the National Institute for Health and Clinical Excellence, the Scottish Intercollegiate Guidelines Network, and the UK Cochrane Centre, met as members of the development group for the Database of Uncertainties about the Effects of Treatments (see bmj.com for details on all participating organisations). Our aim was to discuss the state of research recommendations within our organisations and to develop guidelines for improving the presentation of proposals for further research. All organisations had found weaknesses in the way researchers and authors of systematic reviews and clinical guidelines stated the need for further research. As part of the project, a member of the Centre for Reviews and Dissemination under-took a rapid literature search to identify information on research recommendation models, which found some individual methods but no group initiatives to attempt to standardise recommendations.

Suggested format for research recommendations on the effects of treatments

Core elements.

E Evidence (What is the current state of the evidence?)

P Population (What is the population of interest?)

I Intervention (What are the interventions of interest?)

C Comparison (What are the comparisons of interest?)

O Outcome (What are the outcomes of interest?)

T Time stamp (Date of recommendation)

Optional elements

d Disease burden or relevance

t Time aspect of core elements of EPICOT

s Appropriate study type according to local need

In January 2006, the National Coordinating Centre for Health Technology Assessment presented the findings of an initial comparative analysis of how different organisations currently structure their research recommendations. The National Institute for Health and Clinical Excellence and the National Coordinating Centre for Health Technology Assessment request authors to present recommendations in a four component format for formulating well built clinical questions around treatments: population, intervention, comparison, and outcomes (PICO). 1 In addition, the research recommendation is dated and authors are asked to provide the current state of the evidence to support the proposal.

Clinical Evidence , although not directly standardising its sections for research recommendations, presents gaps in the evidence using a slightly extended version of the PICO format: evidence, population, intervention, comparison, outcomes, and time (EPICOT). Clinical Evidence has used this inherent structure to feed research recommendations on interventions categorised as “unknown effectiveness” back to the National Coordinating Centre for Health Technology Assessment and for inclusion in the Database of Uncertainties about the Effects of Treatments ( http://www.duets.nhs.uk/ ).

We decided to propose the EPICOT format as the basis for its statement on formulating research recommendations and tested this proposal through discussion and example. We agreed that this set of components provided enough context for formulating research recommendations without limiting researchers. In order for the proposed framework to be flexible and more widely applicable, the group discussed using several optional components when they seemed relevant or were proposed by one or more of the group members. The final outcome of discussions resulted in the proposed EPICOT+ format (box).

A recent BMJ article highlighted how lack of research hinders the applicability of existing guidelines to patients in primary care who have had a stroke or transient ischaemic attack. 2 Most research in the area had been conducted in younger patients with a recent episode and in a hospital setting. The authors concluded that “further evidence should be collected on the efficacy and adverse effects of intensive blood pressure lowering in representative populations before we implement this guidance [from national and international guidelines] in primary care.” Table 1 outlines how their recommendations could be formulated using the EPICOT+ format. The decision on whether additional research is indeed clinically and ethically warranted will still lie with the organisation considering commissioning the research.

Research recommendation based on gap in the evidence identified by a cross sectional study of clinical guidelines for management of patients who have had a stroke

• View inline

Table 2 shows the use of EPICOT+ for an unanswered question on the effectiveness of compliance therapy in people with schizophrenia, identified by the Database of Uncertainties about the Effects of Treatments.

Research recommendation based on a gap in the evidence on treatment of schizophrenia identified by the Database of Uncertainties about the Effects of Treatments

Discussions around optional elements

Although the group agreed that the PICO elements should be core requirements for a research recommendation, intense discussion centred on the inclusion of factors defining a more detailed context, such as current state of evidence (E), appropriate study type (s), disease burden and relevance (d), and timeliness (t).

Initially, group members interpreted E differently. Some viewed it as the supporting evidence for a research recommendation and others as the suggested study type for a research recommendation. After discussion, we agreed that E should be used to refer to the amount and quality of research supporting the recommendation. However, the issue remained contentious as some of us thought that if a systematic review was available, its reference would sufficiently identify the strength of the existing evidence. Others thought that adding evidence to the set of core elements was important as it provided a summary of the supporting evidence, particularly as the recommendation was likely to be abstracted and used separately from the review or research that led to its formulation. In contrast, the suggested study type (s) was left as an optional element.

A research recommendation will rarely have an absolute value in itself. Its relative priority will be influenced by the burden of ill health (d), which is itself dependent on factors such as local prevalence, disease severity, relevant risk factors, and the priorities of the organisation considering commissioning the research.

Similarly, the issue of time (t) could be seen to be relevant to each of the core elements in varying ways—for example, duration of treatment, length of follow-up. The group therefore agreed that time had a subsidiary role within each core item; however, T as the date of the recommendation served to define its shelf life and therefore retained individual importance.

Applicability and usability

The proposed statement on research recommendations applies to uncertainties of the effects of any form of health intervention or treatment and is intended for research in humans rather than basic scientific research. Further investigation is required to assess the applicability of the format for questions around diagnosis, signs and symptoms, prognosis, investigations, and patient preference.

When the proposed format is applied to a specific research recommendation, the emphasis placed on the relevant part(s) of the EPICOT+ format may vary by author, audience, and intended purpose. For example, a recommendation for research into treatments for transient ischaemic attack may or may not define valid outcome measures to assess quality of life or gather data on adverse effects. Among many other factors, its implementation will also depend on the strength of current findings—that is, strong evidence may support a tightly focused recommendation whereas a lack of evidence would result in a more general recommendation.

The controversy within the group, especially around the optional components, reflects the different perspectives of the participating organisations—whether they were involved in commissioning, undertaking, or summarising research. Further issues will arise during the implementation of the proposed format, and we welcome feedback and discussion.

Summary points

No common guidelines exist for the formulation of recommendations for research on the effects of treatments

Major organisations involved in commissioning or summarising research compared their approaches and agreed on core questions

The essential items can be summarised as EPICOT+ (evidence, population, intervention, comparison, outcome, and time)

Further details, such as disease burden and appropriate study type, should be considered as required

We thank Patricia Atkinson and Jeremy Wyatt.

Contributors and sources All authors contributed to manuscript preparation and approved the final draft. NJH is the guarantor.

Competing interests None declared.

• Richardson WS ,
• Wilson MC ,
• Nishikawa J ,
• Hayward RSA
• McManus RJ ,
• Leonardi-Bee J ,
• PROGRESS Collaborative Group
• Warburton E
• Rothwell P ,
• McIntosh AM ,
• Lawrie SM ,
• Stanfield AC
• O'Donnell C ,
• Donohoe G ,
• Sharkey L ,
• Jablensky A ,
• Sartorius N ,
• Ernberg G ,

Conducting Biosocial Surveys: Collecting, Storing, Accessing, and Protecting Biospecimens and Biodata (2010)

Chapter: 5 findings, conclusions, and recommendations, 5 findings, conclusions, and recommendations.

A s the preceding chapters have made clear, incorporating biological specimens into social science surveys holds great scientific potential, but also adds a variety of complications to the tasks of both individual researchers and institutions. These complications arise in a number of areas, including collecting, storing, using, and distributing biospecimens; sharing data while protecting privacy; obtaining informed consent from participants; and engaging with Institutional Review Boards (IRBs). Any effort to make such research easier and more effective will need to address the issues in these areas.

In considering its recommendations, the panel found it useful to think of two categories: (1) recommendations that apply to individual investigators, and (2) recommendations that are addressed to the National Institute on Aging (NIA) or other institutions, particularly funding agencies. Researchers who wish to collect biological specimens with social science data will need to develop new skills in a variety of areas, such as the logistics of specimen storage and management, the development of more diverse informed consent forms, and ways of dealing with the disclosure risks associated with sharing biogenetic data. At the same time, NIA and other funding agencies must provide researchers the tools they need to succeed. These tools include such things as biorepositories for maintaining and distributing specimens, better guidance on informed consent policies, and better ways to share data without risking confidentiality.

TAKING ADVANTAGE OF EXISTING EXPERTISE

Although working with biological specimens will be new and unfamiliar to many social scientists, it is an area in which biomedical researchers have a great deal of expertise and experience. Many existing documents describe recommended procedures and laboratory practices for the handling of biospecimens. These documents provide an excellent starting point for any social scientist who is interested in adding biospecimens to survey research.

Recommendation 1: Social scientists who are planning to add biological specimens to their survey research should familiarize themselves with existing best practices for the collection, storage, use, and distribution of biospecimens. First and foremost, the design of the protocol for collec tion must ensure the safety of both participants and survey staff (data and specimen collectors and handlers).

Although existing best-practice documents were not developed with social science surveys in mind, their guidelines have been field-tested and approved by numerous IRBs and ethical oversight committees. The most useful best-practice documents are updated frequently to reflect growing knowledge and changing opinions about the best ways to collect, store, use, and distribute biological specimens. At the same time, however, many issues arising from the inclusion of biospecimens in social science surveys are not fully addressed in the best-practice documents intended for biomedical researchers. For guidance on these issues, it will be necessary to seek out information aimed more specifically at researchers at the intersection of social science and biomedicine.

COLLECTING, STORING, USING, AND DISTRIBUTING BIOSPECIMENS

As described in Chapter 2 , the collection, storage, use, and distribution of biospecimens and biodata are tasks that are likely to be unfamiliar to many social scientists and that raise a number of issues with which even specialists are still grappling. For example, which biospecimens in a repository should be shared, given that in most cases the amount of each specimen is limited? And given that the available technology for cost-efficient analysis of biospecimens, particularly genetic analysis, is rapidly improving, how much of any specimen should be used for immediate research and analysis, and how much should be stored for analysis at a later date? Collecting, storing, using, and distributing biological specimens also present significant practical and financial challenges for social scientists. Many of the questions they must address, such as exactly what should be held, where it should be held, and what should be shared or distributed, have not yet been resolved.

Developing Data Sharing Plans

An important decision concerns who has access to any leftover biospecimens. This is a problem more for biospecimens than for biodata because in most cases, biospecimens can be exhausted. Should access be determined according to the principle of first funded, first served? Should there be a formal application process for reviewing the scientific merits of a particular investigation? For studies that involve international collaboration, should foreign investigators have access? And how exactly should these decisions be made? Recognizing that some proposed analyses may lie beyond the competence of the original investigators, as well as the possibility that principal investigators may have a conflict of interest in deciding how to use any remaining biospecimens, one option is for a principal investigator to assemble a small scientific committee to judge the merits of each application, including the relevance of the proposed study to the parent study and the capacities of the investigators. Such committees should publish their review criteria to help prospective applicants. A potential problem with such an approach, however, is that many projects may not have adequate funding to carry out such tasks.

Recommendation 2: Early in the planning process, principal investigators who will be collecting biospecimens as part of a social science survey should develop a complete data sharing plan.

This plan should spell out the criteria for allowing other researchers to use (and therefore deplete) the available stock of biospecimens, as well as to gain access to any data derived therefrom. To avoid any appearance of self-interest, a project might empower an external advisory board to make decisions about access to its data. The data sharing plan should also include provisions for the storage and retrieval of biospecimens and clarify how the succession of responsibility for and control of the biospecimens will be handled at the conclusion of the project.

Recommendation 3: NIA (or preferably the National Institutes of Health [NIH]) should publish guidelines for principal investigators containing a list of points that need to be considered for an acceptable data sharing plan. In addition to staff review, Scientific Review Panels should read and comment on all proposed data sharing plans. In much the same way as an unacceptable human subjects plan, an inadequate data sharing plan should hold up an otherwise acceptable proposal.

Supporting Social Scientists in the Storage of Biospecimens

The panel believes that many social scientists who decide to add the collection of biospecimens to their surveys may be ill equipped to provide for the storage and distribution of the specimens.

Conclusion: The issues related to the storage and distribution of biospecimens are too complex and involve too many hidden costs to assume that social scientists without suitable knowledge, experience, and resources can handle them without assistance.

Investigators should therefore have the option of delegating the storage and distribution of biospecimens collected as part of social science surveys to a centralized biorepository. Depending on the circumstances, a project might choose to utilize such a facility for immediate use, long-term or archival storage, or not at all.

Recommendation 4: NIA and other relevant funding agencies should support at least one central facility for the storage and distribution of biospecimens collected as part of the research they support.

PROTECTING PRIVACY AND CONFIDENTIALITY: SHARING DIGITAL REPRESENTATIONS OF BIOLOGICAL AND SOCIAL DATA

Several different types of data must be kept confidential: survey data, data derived from biospecimens, and all administrative and operational data. In the discussion of protecting confidentiality and privacy, this report has focused on biodata, but the panel believes it is important to protect all the data collected from survey participants. For many participants, for example, data on wealth, earnings, or sexual behavior can be as or more sensitive than genetic data.

Conclusion: Although biodata tend to receive more attention in discussions of privacy and confidentiality, social science and operational data can be sensitive in their own right and deserve similar attention in such discussions.

Protecting the participants in a social science survey that collects biospecimens requires securing the data, but data are most valuable when they are made available to researchers as widely as possible. Thus there is an inherent tension between the desire to protect the privacy of the participants and the desire to derive as much scientific value from the data as possible, particularly since the costs of data collection and analysis are so high. The following recommendations regarding confidentiality are made in the spirit of balancing these equally important needs.

Genomic data present a particular challenge. Several researchers have demonstrated that it is possible to identify individuals with even modest amounts of such data. When combined with social science data, genomic data may pose an even greater risk to confidentiality. It is difficult to know how much or which genomic data, when combined with social science data, could become critical identifiers in the future. Although the problem is most significant with genomic data, similar challenges can arise with other kinds of data derived from biospecimens.

Conclusion: Unrestricted distribution of genetic and other biodata risks violating promises of confidentiality made to research participants.

There are two basic approaches to protecting confidentiality: restricting data and restricting access. Restricting data—for example, by stripping individual and spatial identifiers and modifying the data to make it difficult or impossible to trace them back to their source—usually makes it possible to release social science data widely. In the case of biodata, however, there is no answer to how little data is required to make a participant uniquely identifiable. Consequently, any release of biodata must be carefully managed to protect confidentiality.

Recommendation 5: No individual-level data containing uniquely identify ing variables, such as genomic data, should be publicly released without explicit informed consent.

Recommendation 6: Genomic data and other individual-level data con taining uniquely identifying variables that are stored or in active use by investigators on their institutional or personal computers should be encrypted at all times.

Even if specific identifying variables, such as names and addresses, are stripped from data, it is still often possible to identify the individuals associated with the data by other means, such as using the variables that remain (age, sex, marital status, family income, etc.) to zero in on possible candidates. In the case of biodata that do not uniquely identify individuals and can change with time, such as blood pressure and physical measurements, it may be possible to share the data with no more protection than stripping identifying variables. Even these data, however, if known to intruders, can increase identification disclosure risk when combined with enough other data. With sufficient characteristics to match, intruders can uniquely identify individuals in shared data if given access to another data source that contains the same information plus identifiers.

Conclusion: Even nonunique biodata, if combined with social science data, may pose a serious risk of reidentification.

In the case of high-dimensional genomic data, standard disclosure limitation techniques, such as data perturbation, are not effective with respect to preserving the utility of the data because they involve such extreme alterations that they would severely distort analyses aimed at determining gene–gene and gene–environment interactions. Standard disclosure limitation methods could be used to generate public-use data sets that would enable low-dimensional analyses involving genes, for example, one gene at a time. However, with several such public releases, it may be possible for a key match to be used to construct a data set with higher-dimensional genomic data.

Conclusion: At present, no data restriction strategy has been demonstrated to protect confidentiality while preserving the usefulness of the data for drawing inferences involving high-dimensional interactions among genomic and social science variables, which are increasingly the target of research. Providing public-use genomic data requires such intense data masking to protect confidentiality that it would distort the high-dimensional analyses that could result in ground-breaking research progress.

Recommendation 7: Both rich genomic data acquired for research and sensitive and potentially identifiable social science data that do not change (or change very little) with time should be shared only under restricted circumstances, such as licensing and (actual or virtual) data enclaves.

As discussed in Chapter 3 , the four basic ways to restrict access to data are licensing, remote execution centers, data enclaves, and virtual data enclaves. Each has its advantages and disadvantages. 1 Licensing, for example, is the least restrictive for a researcher in terms of access to the data, but the licensing process itself can be lengthy and burdensome. Thus it would be useful if the licensing process could be facilitated.

Recommendation 8: NIA (or preferably NIH) should develop new stan dards and procedures for licensing confidential data in ways that will maximize timely access while maintaining security and that can be used by data repositories and by projects that distribute data.

Ways to improve the other approaches to restricted access are needed as well. For example, improving the convenience and availability of virtual data enclaves could increase the use of combined social science and biodata without

a significant increase in risk to confidentiality. The panel notes that much of the discussion of the confidentiality risk posed by the various approaches is theoretical; no one has a clear idea of just what disclosure risks are associated with the various ways of sharing data. It is important to learn more about these disclosure risks for a variety of reasons—determining how to minimize the risks, for instance, or knowing which approaches to sharing data pose the least risk. It would also be useful to be able to describe disclosure risks more accurately to survey participants.

Recommendation 9: NIA and other funding agencies should assess the strength of confidentiality protections through periodic expert audits of confidentiality and computer security. Willingness to participate in such audits should be a condition for receipt of NIA support. Beyond enforce ment, the purpose of such audits would be to identify challenges and solutions.

Evaluating risks and applying protection methods, whether they involve restricted access or restricted data, is a complex process requiring expertise in disclosure protection methods that exceeds what individual principal investigators and their institutions usually possess. Currently, not enough is known to be able to represent these risks either fully or accurately. The NIH requirement for data sharing necessitates a large investment of resources to anticipate which variables are potentially available to intruders and to alter data in ways that reduce disclosure risks while maintaining the utility of the data. Such resources are better spent by principal investigators on collecting and analyzing the data.

Recommendation 10: NIH should consider funding Centers of Excellence to explore new ways of protecting digital representations of data and to assist principal investigators wishing to share data with others. NIH should also support research on disclosure risks and limitations.

Principal investigators could send digital data to these centers, which would organize and manage any restricted access or restricted data policies or provide advisory services to investigators. NIH would maintain the authority to penalize those who violated any confidentiality agreements, for example, by denying them or their home institution NIH funding. Models for these centers include the Inter-university Consortium for Political and Social Research (ICPSR) and its projects supported by NIH and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) and the UK data sharing archive. The centers would alleviate the burden of data sharing as mandated of principal investigators by NIH and place it in expert hands. However, excellence in the design of data access and control systems

is likely to require intimate knowledge of each specific data resource, so data producers should be involved in the systems’ development.

INFORMED CONSENT

As described in Chapter 4 , informed consent is a complex subject involving many issues that are still being debated; the growing power of genetic analysis techniques and bioinformatics has only added to this complexity. Given the rapid pace of advances in scientific knowledge and in the technology used to analyze biological materials, it is impossible to predict what information might be gleaned from biological specimens just a few years hence; accordingly, it is impossible, even in theory, to talk about perfectly informed consent. The best one can hope for is relatively well-informed consent from a study’s participants, but knowing precisely what that means is difficult. Determining the scope of informed consent adds another layer of complexity. Will new analyses be covered under the existing consent, for example? There are no clear guidelines on such questions, yet specific details on the scope of consent will likely affect an IRB’s reaction to a study proposal.

What Individual Researchers Need to Know and Do Regarding Informed Consent

To be sure, there is a wide range of views about the practicality of providing adequate protection to participants while proceeding with the scientific enterprise, from assertions that it is simply not possible to provide adequate protection to offers of numerous procedural safeguards but no iron-clad guarantees. This report takes the latter position—that investigators should do their best to communicate adequately and accurately with participants, to provide procedural safeguards to the extent possible, and not to promise what is not possible. 2 Social science researchers need to know that adding the collection of biospecimens to social science surveys changes the nature of informed consent. Informed consent for a traditional social science survey may entail little more than reading a short script over the phone and asking whether the participant is willing to continue; obtaining informed consent for the collection and use of biospecimens and biodata is generally a much more involved process.

Conclusion: Social scientists should be made aware that the process of obtaining informed consent for the use of biospecimens and biodata typically differs from social science norms.

If participants are to provide truly informed consent to taking part in any study, they must be given a certain minimum amount of information. They should be told, for example, what the purpose of the study is, how it is to be carried out, and what participants’ roles are. In addition, because of the unique risks associated with providing biospecimens, participants in a social science survey that involves the collection of such specimens should be provided with other types of information as well. In particular, they should be given detail on the storage and use of the specimens that relates to those risks and can assist them in determining whether to take part in the study.

Recommendation 11: In designing a consent form for the collection of biospecimens, in addition to those elements that are common to social science and biomedical research, investigators should ensure that certain other information is provided to participants:

how long researchers intend to retain their biospecimens and the genomic and other biodata that may be derived from them;

both the risks associated with genomic data and the limits of what they can reveal;

which other researchers will have access to their specimens, to the data derived therefrom, and to information collected in a survey questionnaire;

the limits on researchers’ ability to maintain confidentiality;

any potential limits on participants’ ability to withdraw their speci mens or data from the research;

the penalties 3 that may be imposed on researchers for various types of breaches of confidentiality; and

what plans have been put in place to return to them any medically relevant findings.

Researchers who fail to properly plan for and handle all of these issues before proceeding with a study are in essence compromising assurances under informed consent. The literature on informed consent emphasizes the importance of ensuring that participants understand reasonably well what they are consenting to. This understanding cannot be taken for granted, particularly as it pertains to the use of biological specimens and the data derived therefrom.

While it is not possible to guarantee that participants have a complete understanding of the scientific uses of their specimens or all the possible risks of their participation, they should be able to make a relatively well-informed decision about whether to take part in the study. Thus the ability of various participants to understand the research and the informed consent process must be considered. Even impaired individuals may be able to participate in research if their interests are protected and they can do so only through proxy consent. 4

Recommendation 12: NIA should locate and publicize positive examples of the documentation of consent processes for the collection of biospeci mens. In particular, these examples should take into account the special needs of certain individuals, such as those with sensory problems and the cognitively impaired.

Participants in a biosocial survey are likely to have different levels of comfort concerning how their biospecimens and data will be used. Some may be willing to provide only answers to questions, for example, while others may both answer questions and provide specimens. Among those who provide specimens, some may be willing for the specimens to be used only for the current study, while others may consent to their use in future studies. One effective way to deal with these different comfort levels is to offer a tiered approach to consent that allows participants to determine just how their specimens and data will be used. Tiers might include participating in the survey, providing specimens for genetic and/or nongenetic analysis in a particular study, and allowing the specimens and data to be stored for future uses (genetic and/or nongenetic). For those participants who are willing to have their specimens and data used in future studies, researchers should tell them what sort of approval will be obtained for such use. For example, an IRB may demand reconsent, in which case participants may have to be contacted again before their specimens and data can be used. Ideally, researchers should design their consent forms to avoid the possibility that an IRB will demand a costly or infeasible reconsent process.

Recommendation 13: Researchers should consider adopting a tiered approach to obtaining consent. Participants who are willing to have their specimens and data used in future studies should be informed about the process that will be used to obtain approval for such uses.

What Institutions Should Do Regarding Informed Consent

Because the details of informed consent vary from study to study, individual investigators must bear ultimate responsibility for determining the details of informed consent for any particular study. Thus researchers must understand the various issues and concerns surrounding informed consent and be prepared to make decisions about the appropriate approach for their research in consultation with staff of survey organizations. These decisions should be addressed in the training of survey interviewers. As noted above, however, the issues surrounding informed consent are complex and not completely resolved, and researchers have few options for learning about informed consent as it applies to social science studies that collect biospecimens. Thus it makes sense for agencies funding this research, the Office for Human Research Protection (OHRP), or other appropriate organizations (for example, Public Responsibility in Medicine and Research [PRIM&R]) to provide opportunities for such learning, taking into account the fact that the issues arising in biosocial research do not arise in the standard informed consent situations encountered in social science research. It should also be made clear that the researchers’ institution is usually deemed (e.g., in the courts) to bear much of the responsibility for informed consent.

Recommendation 14: NIA, OHRP, and other appropriate organizations should sponsor training programs, create training modules, and hold informational workshops on informed consent for investigators, staff of survey organizations, including field staff, administrators, and mem bers of IRBs who oversee surveys that collect social science data and biospecimens.

The Return of Medically Relevant Information

An issue related to informed consent is how much information to provide to survey participants once their biological specimens have been analyzed and in particular, how to deal with medically relevant information that may arise from the analysis. What, for example, should a researcher do if a survey participant is found to have a genetic disease that does not appear until later in life? Should the participant be notified? Should participants be asked as part of the initial interview whether they wish to be notified about such a discovery? At this time, there are no generally agreed-upon answers to such questions, but researchers should expect to have to deal with these issues as they analyze the data derived from biological specimens.

Recommendation 15: NIH should direct investigators to formulate a plan in advance concerning the return of any medically relevant findings to

survey participants and to implement that plan in the design and conduct of their informed consent procedures.

INSTITUTIONAL REVIEW BOARDS

Investigators seeking IRB approval for biosocial research face a number of challenges. Few IRBs are familiar with both social and biological science; thus, investigators may find themselves trying to justify standard social science protocols to a biologically oriented IRB or explaining standard biological protocols to an IRB that is used to dealing with social science—or sometimes both. Researchers can expect these obstacles, which arise from the interdisciplinary nature of their work, to be exacerbated by a number of other factors that are characteristic of IRBs in general (see Chapter 4 ).

Recommendation 16: In institutions that have separate biomedical and social science IRBs, mechanisms should be created for sharing expertise during the review of biosocial protocols. 5

What Individual Researchers Need to Do Regarding IRBs

Because the collection of biospecimens as part of social science surveys is still relatively unfamiliar to many IRBs, researchers planning such a study can expect their interactions with the IRB overseeing the research to involve a certain learning curve. The IRB may need extra time to become familiar and comfortable with the proposed practices of the survey, and conversely, the researchers will need time to learn what the IRB will require. Thus it will be advantageous if researchers conducting such studies plan from the beginning to devote additional time to working with their IRBs.

Recommendation 17: Investigators considering collecting biospecimens as part of a social science survey should consult with their IRBs early and often.

What Research Agencies Should Do Regarding IRBs

One way to improve the IRB process would be to give members of IRBs an opportunity to learn more about biosocial research and the risks it entails.

This could be done by individual institutions, but it would be more effective if a national funding agency took the lead (see Recommendation 14).

It is the panel’s hope that its recommendations will support the incorporation of social science and biological data into empirical models, allowing researchers to better document the linkages among social, behavioral, and biological processes that affect health and other measures of well-being while avoiding or minimizing many of the challenges that may arise. Implementing these recommendations will require the combined efforts of both individual investigators and the agencies that support them.

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Recent years have seen a growing tendency for social scientists to collect biological specimens such as blood, urine, and saliva as part of large-scale household surveys. By combining biological and social data, scientists are opening up new fields of inquiry and are able for the first time to address many new questions and connections. But including biospecimens in social surveys also adds a great deal of complexity and cost to the investigator's task. Along with the usual concerns about informed consent, privacy issues, and the best ways to collect, store, and share data, researchers now face a variety of issues that are much less familiar or that appear in a new light.

In particular, collecting and storing human biological materials for use in social science research raises additional legal, ethical, and social issues, as well as practical issues related to the storage, retrieval, and sharing of data. For example, acquiring biological data and linking them to social science databases requires a more complex informed consent process, the development of a biorepository, the establishment of data sharing policies, and the creation of a process for deciding how the data are going to be shared and used for secondary analysis--all of which add cost to a survey and require additional time and attention from the investigators. These issues also are likely to be unfamiliar to social scientists who have not worked with biological specimens in the past. Adding to the attraction of collecting biospecimens but also to the complexity of sharing and protecting the data is the fact that this is an era of incredibly rapid gains in our understanding of complex biological and physiological phenomena. Thus the tradeoffs between the risks and opportunities of expanding access to research data are constantly changing.

Conducting Biosocial Surveys offers findings and recommendations concerning the best approaches to the collection, storage, use, and sharing of biospecimens gathered in social science surveys and the digital representations of biological data derived therefrom. It is aimed at researchers interested in carrying out such surveys, their institutions, and their funding agencies.

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Analysis of Current Recommendation Techniques and Evaluation Metrics to Design an Improved Book Recommendation System

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• Sushma Malik 40 ,
• Anamika Rana 41 &
• Mamta Bansal 40  

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 860))

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Sometimes users face difficulty in searching required content on the digital platform due to an enοrmous amοunt of information. But this hassle may be solved with the assist of a recοmmender system (RS). RS plays a fundamental role in reducing information overloading. It also provides the item based on the interest of the user. RS plays a significant role on E-commerce sites, online auction, and on any online platform. The Bοok recοmmender system (BRS) is now mostly used by books e-commerce sites. This paper surveyed the machine learning techniques which have been implemented to design the BRS. Six types of approaches are identified to design the BRS like clustering, Collaborative Filtering, Content-Based, Association, Opinion Mining, and Hybrid technique. The hybrid technique is designed by the cοmbination of various techniques.

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Malik, S., Rana, A., Bansal, M. (2022). Analysis of Current Recommendation Techniques and Evaluation Metrics to Design an Improved Book Recommendation System. In: Mallick, P.K., Bhoi, A.K., González-Briones, A., Pattnaik, P.K. (eds) Electronic Systems and Intelligent Computing. Lecture Notes in Electrical Engineering, vol 860. Springer, Singapore. https://doi.org/10.1007/978-981-16-9488-2_49

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• Review Article
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• Published: 16 May 2024

CHRONIC LYMPHOCYTIC LEUKEMIA

ERIC recommendations for TP53 mutation analysis in chronic lymphocytic leukemia—2024 update

• Jitka Malcikova   ORCID: orcid.org/0000-0003-3650-6698 1 , 2   na1 ,
• Sarka Pavlova   ORCID: orcid.org/0000-0003-1528-9743 1 , 2   na1 ,
• Panagiotis Baliakas   ORCID: orcid.org/0000-0002-5634-7156 3 ,
• Thomas Chatzikonstantinou   ORCID: orcid.org/0000-0003-4105-1253 4 ,
• Eugen Tausch 5 ,
• Mark Catherwood 6 ,
• Davide Rossi 7 ,
• Thierry Soussi   ORCID: orcid.org/0000-0001-8184-3293 3 , 8 ,
• Boris Tichy 2 ,
• Arnon P. Kater   ORCID: orcid.org/0000-0003-3190-1891 9 ,
• Carsten U. Niemann   ORCID: orcid.org/0000-0001-9880-5242 10 ,
• Frederic Davi 11 , 12 ,
• Gianluca Gaidano   ORCID: orcid.org/0000-0002-4681-0151 13 ,
• Stephan Stilgenbauer   ORCID: orcid.org/0000-0002-6830-9296 5 ,
• Richard Rosenquist   ORCID: orcid.org/0000-0002-0211-8788 14 , 15 ,
• Kostas Stamatopoulos   ORCID: orcid.org/0000-0001-8529-640X 4 ,
• Paolo Ghia   ORCID: orcid.org/0000-0003-3750-7342 16 , 17   na2 &
• Sarka Pospisilova   ORCID: orcid.org/0000-0001-7136-2680 1 , 2   na2  

Leukemia ( 2024 ) Cite this article

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• Risk factors

In chronic lymphocytic leukemia (CLL), analysis of TP53 aberrations (deletion and/or mutation) is a crucial part of treatment decision-making algorithms. Technological and treatment advances have resulted in the need for an update of the last recommendations for TP53 analysis in CLL, published by ERIC, the European Research Initiative on CLL, in 2018. Based on the current knowledge of the relevance of low-burden TP53 -mutated clones, a specific variant allele frequency (VAF) cut-off for reporting TP53 mutations is no longer recommended, but instead, the need for thorough method validation by the reporting laboratory is emphasized. The result of TP53 analyses should always be interpreted within the context of available laboratory and clinical information, treatment indication, and therapeutic options. Methodological aspects of introducing next-generation sequencing (NGS) in routine practice are discussed with a focus on reliable detection of low-burden clones. Furthermore, potential interpretation challenges are presented, and a simplified algorithm for the classification of TP53 variants in CLL is provided, representing a consensus based on previously published guidelines. Finally, the reporting requirements are highlighted, including a template for clinical reports of TP53 aberrations. These recommendations are intended to assist diagnosticians in the correct assessment of TP53 mutation status, but also physicians in the appropriate understanding of the lab reports, thus decreasing the risk of misinterpretation and incorrect management of patients in routine practice whilst also leading to improved stratification of patients with CLL in clinical trials.

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Clinical significance of TP53, BIRC3, ATM and MAPK-ERK genes in chronic lymphocytic leukaemia: data from the randomised UK LRF CLL4 trial

Clinical impact of panel-based error-corrected next generation sequencing versus flow cytometry to detect measurable residual disease (MRD) in acute myeloid leukemia (AML)

Different prognostic impact of recurrent gene mutations in chronic lymphocytic leukemia depending on IGHV gene somatic hypermutation status: a study by ERIC in HARMONY

Clinical impact of tp53 alterations in patients with cll.

A TP53 aberration is defined as either the deletion of the TP53 gene locus on 17p13 [del(17p)] or the presence of a mutation, i.e., somatic change in the sequence of the TP53 gene ( TP53 mut). The frequency of TP53 aberrations in patients with chronic lymphocytic leukemia (CLL) is higher in those with unmutated immunoglobulin heavy variable (IGHV) genes. Generally, the frequency is low at diagnosis (5-10% of patients, depending on the method used), it is slightly higher in cohorts of patients entering frontline treatment (10–20%; Fig.  1 ), and further increases in later disease stages, predominantly in chemoimmunotherapy (CIT)-treated patients and Richter transformation (up to 50%) [ 1 , 2 , 3 ]. In patients with CLL, del(17p) is mostly accompanied by TP53 mutations, and sole del(17p) is infrequent, while sole TP53 mutations are more commonly found (Fig.  1 ) [ 4 , 5 , 6 , 7 , 8 , 9 , 10 ].

Values were adopted from published studies employing ultra-deep NGS to detect TP53 mutations [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 66 ]. High VAF—variants >10% VAF, low VAF—variants 1–10% VAF, except for two studies where variants <1% and >1% could not be distinguished [ 4 , 5 ]. In patients with high VAF TP53 mutations, co-existence of del(17p) prevails. * In patients carrying low VAF TP53 mutation concomitant del(17p) is detected in only a minority of cases, but the true status is unknown due to the higher detection limit of FISH (>5% aberrant nuclei). The breakdown depicted here corresponds to pre-treatment cohorts (diagnosis or before frontline treatment). In the chemo-pretreated cohorts the proportion of patients with TP53 defects can reach 40% [ 1 , 20 , 106 ].

Prognostic value of TP53 alterations

In the early 1990s, several studies reported the prognostic relevance of TP53 aberrations [ 11 , 12 , 13 , 14 ]. Subsequently, in the Döhner hierarchical model, del(17p) was classified as the most adverse cytogenetic abnormality [ 15 ]. These findings were further underpinned by many studies [ 16 , 17 , 18 , 19 ], including clinical trials [ 20 , 21 , 22 ], highlighting the independent role of both del(17p) and TP53 mutations.

The prognostic value of TP53 aberrations is evident early in the course of CLL. Several prognostic scores developed to predict time-to-first-treatment (TTFT) include TP53 aberrations as a variable. In the CLL1 trial, del(17p) conferred a shorter TTFT and was given the highest score in a weighted point system of variables (CLL1 prognostic model) [ 23 , 24 ]. Similarly, the CLL international prognostic index (CLL-IPI) and the CLL WithOut Need of Treatment (CLL-WONT) incorporate TP53 aberrations as an independent predictor of shorter TTFT [ 25 , 26 ]. Conversely, TP53 aberrations failed to predict TTFT in the training cohort of the International Prognostic Score for Early-stage CLL (IPS-E) [ 27 ]. This finding was attributed to the differential impact of TP53 aberrations on TTFT based on the mutational status of the IGHV genes. Further supporting this reasoning, a recent ERIC study and a single center study from MD Anderson revealed that TP53 aberrations predict TTFT only in patients with unmutated IGHV genes [ 28 , 29 ].

TP53 aberrations also have paramount prognostic value in treated patients with CLL since, generally, they confer a worse prognosis with all available treatments, including agents targeting B cell receptor (BcR) signaling and BCL2, at least in the relapsed/refractory setting [ 30 , 31 , 32 , 33 ]. Interestingly, TP53 aberration status may potentially affect targeted treatment outcomes differently compared to CIT. In particular, the prognostic value of single-hit TP53 (isolated del(17p) or sole TP53 mutation) remains unclear with targeted agents, while concomitant TP53 mutations and del(17p) (multi-hit TP53 ) appear to be independently associated with worse outcomes in some of the studies [ 34 , 35 , 36 , 37 ]. However, since in many published studies del(17p) and TP53 mutations were not distinguished [ 32 , 33 ], and in some only del(17p) was included [ 38 ], this relevant issue is currently inconclusive. Moreover, the presence of homozygous mutations has not been considered at all. Thus, it is now imperative to include definitions of the type, clonal burden, and number of TP53 defects in clinical trials and academic studies in order to be able to provide a uniform classification, similar to myeloid neoplasms [ 39 ].

Predictive value of TP53 alterations

The predictive value of TP53 aberrations is clear when CIT regimens are included among the treatment options: in fact, targeted agents as either monotherapy or in combination outperformed CIT regimens in the frontline and R/R settings [ 33 , 40 , 41 , 42 , 43 ] and represent the preferred option for these patients [ 44 ].

On the contrary, the role of TP53 aberrations in choosing between targeted agent regimens is less well studied. In the ALPINE trial, zanubrutinib conferred a better PFS than ibrutinib in all R/R patients including those with del(17p)/ TP53 mut [ 45 ], while in the ELEVATE-RR trial, no superiority of acalabrutinib vs. ibrutinib was observed [ 46 ]. Except for these findings, conclusions about the predictive value of TP53 aberrations are based on cross-trial comparisons in which the prognostic impact of TP53 on PFS appears to be stronger with time-limited regimens [ 42 , 47 ] than with continuous therapy [ 40 ]. Nevertheless, the lack of direct comparisons precludes definitive conclusions from being drawn at present.

Relevance of low-burden TP53 mutations

The advent of next-generation sequencing (NGS) in routine practice allowed the detection of clones carrying variants below the detection limit of Sanger sequencing, which was arbitrarily set to 10% variant allele frequency (VAF). When referring to such clones (<10% VAF), it is recommended to use the terms “low-burden,” minor-clone,” “low-VAF,” or “low-level,” and to avoid the terminology “subclonal,” as this is generally used to describe variants not present in the entire tumor population, as opposed to “clonal” [ 48 ] (Fig.  2 ). Indeed, it is impossible to define the clonality of a TP53 variant if the tumor fraction in the assayed tissue and the ploidy of the TP53 locus are unknown, as is usually the case in molecular diagnostic laboratories.

The distribution of variant allele frequencies (%VAF; y -axis) of TP53 mutations detected in patients with CLL ( x -axis). Variants present in the whole cancer population are clonal, otherwise, they are deemed subclonal. Variants <10% VAF are considered low burden. This distribution is valid when the sample contains >90% tumor cells. In samples with a low CLL cell fraction, a low VAF may, in reality, correspond to a clonal mutation.

The clinical relevance of low-burden TP53 mutations is still debated. The vast majority of evidence was obtained in the era of CIT, and no clinical trial was designed to assess their impact. The conclusions are based mainly on retrospective studies comparing PFS and OS in patients with low-burden TP53 mutations [ 4 , 5 , 6 , 7 , 9 ] and in a single prospective clinical trial, albeit with a different initial endpoint [ 8 ]. The existing evidence mostly, but not uniformly, suggested shortened survival for patients with low-burden TP53 mutations, with the median OS being intermediate between patients having high-burden TP53 mutations and those with intact TP53 [ 4 , 5 , 6 , 7 , 9 ] (Supplementary Table  S1 ). Differing prevailing types of treatment and cohort constitutions mainly contribute to the differences between studies. Some studies analyzed diagnostic or early-stage cohorts with higher proportions of patients with mutated IGHV genes, while TP53 testing is generally indicated in active disease, where unmutated IGHV genes prevails. Prospective assessment of low-burden TP53 mutations in CIT-treated patients is not expected as this type of treatment has been superseded by chemo-free approaches. Nevertheless, independent studies have consistently shown that the small TP53- mutated clones are at a high risk of clonal expansion when treated with genotoxic agents as in CIT regimens [ 5 , 6 , 7 , 49 , 50 ]. In contrast, targeted agents act independently of the p53 pathway and, as such, are assumed not to directly accelerate the expansion of TP53 deficient clones. In line with that, no preferential pattern of clonal evolution of TP53 -aberrant clones was described upon treatment with targeted agents, with all scenarios of clonal development being observed (persistence, expansion, and disappearance) [ 6 , 50 , 51 , 52 , 53 , 54 , 55 ]. Nevertheless, the follow-up is short in many studies, and it is unclear how the TP53 -aberrant clone will evolve after several lines of targeted agents and if the TP53 defect can promote resistance via facilitating genomic instability. Thus, the clinical impact of low-burden TP53 mutations in patients treated with targeted agents is yet to be defined [ 56 ].

From a technical standpoint, it is important to emphasize that not all low-VAF variants are truly low-burden, in particular when samples with a lower proportion of tumor cells are analyzed [ 57 ]. This applies especially to patients with small lymphocytic lymphoma (SLL) or patients with predominantly nodal relapse with limited lymphocytosis. For example, the variant detected in 10% VAF in the unpurified bulk sample can be fully clonal (i.e., present in all cancer cells) if the cancer cell fraction is 20% and there is no loss of heterozygosity [ 57 ].

Altogether, the current consensus is that CIT should be strictly avoided in all patients with TP53 aberrations, irrespective of the clone size. On these grounds, ERIC proposes that no limitation should be set for reporting regarding TP53 -mutant clone size, while at the same time placing a strong emphasis on thorough methodological validation/ verification (Fig.  3 ). More particularly, laboratories should assess their own technical limit of detection and method performance, and describe them in the report (see section – “NGS-based approaches for TP53 mutational analysis in CLL”). The result should always be interpreted in the context of tumor cell content, separation method, and disease phase. In this way, the TP53 report will complement clinical information and patient preferences for an optimal treatment recommendation.

The laboratory is responsible for issuing the correct result and reports all pathogenic TP53 variants above the validated LoD. The result should be interpreted in the context of tumor cell content, separation method, and disease status. The physician decides about the treatment based on all available information: the laboratory results, the clinical characteristics of the patient, patient preferences, and the availability of the treatment.

Procedure description

Methodology for tp53 status evaluation.

Fluorescence in-situ hybridization (FISH) should be employed for the detection of del(17p). A cut-off for a positive result (% of positive nuclei) needs to be assessed for each laboratory, sample type, and processing, and no generally applicable cut-off (e.g. 7%) can be given. Poor technical performance (e.g. low hybridization efficiency) may result in false-positive del(17p) calling. The procedure should follow the European Recommendations and Quality Assurance for Cytogenomic Analysis of Haematological Neoplasms [ 58 ]. The evaluation of del(17p) as a part of NGS-based strategy or array-based techniques is not recommended since the limit of detection for copy-number alterations (CNAs) is currently insufficient (~20% aberrant cells) and may lead to overlooking deletions present in lower cell fractions. It may, however, bring information on concurrent CNAs and disclose copy-neutral loss of heterozygosity (CN-LOH) of the TP53 locus.

ESMO [ 59 ] recommends assessing del(17p) first and then TP53 testing only in cases without del(17p). Following this two-step procedure can be difficult and may cause treatment delays but it may be reasonable in the presence of financial constraints. In addition, the knowledge about both abnormalities might be informative given the above-discussed issue of single vs. multi-hit TP53 aberrations [ 35 , 36 , 60 ]. Therefore, it is preferred to analyze both TP53 gene mutations and locus deletions simultaneously, if possible.

For TP53 variant detection, the preferred methodology is NGS, but Sanger sequencing can still be used if NGS is not available. The main limitations of Sanger sequencing concern its low-throughput performance and the detection limit, that varies between 10–20% VAF and is dependent on sequence context, user experience, and software for the analysis of sequencing chromatograms [ 61 ]. Attention must be paid to checking the primers for the presence of population variants that lead to allelic drop-out and possible failure to detect the mutation (this applies to both Sanger sequencing and amplicon-based NGS). The list of population variants is expanding with increasing knowledge [ 62 ]. Such variants are present within the sequence of some of the previously recommended IARC protocol primers [ 63 ], and these should be used with caution (primers alongside with the information about the population variants are listed in Supplementary Table  S2 ).

The basic approach valid for both Sanger sequencing and NGS for sampling, DNA isolation, and the covered region was described in the recommendations issued by ERIC in 2018 [ 64 ] and is still applicable. The basic principles are summarized in Table  1 including updates discussed below. The following text pinpoints the most important issues and reflects the recent developments in the sequencing methodology and resulting requirements for the quality of the testing, the interpretation, and the reporting.

Sampling and enrichment of cancer cells

Tumor cells should be enriched to avoid VAF underestimation, or even missing a variant. Moreover, when non-separated leukocytes are analyzed using NGS with low detection limit, the detection of small TP53 -aberrant clones not related to CLL, i.e., detection of clonal hematopoiesis of indeterminate potential (CHIP) [ 65 ], cannot be entirely excluded.

Based on the local practice, two approaches for cancer cell enrichment can be adopted. The optimal strategy is the separation of CD19 + cells in all CLL samples that can be performed via positive or negative selection. Negative selection is a more cost-effective approach in most CLL cases, yet might not be affordable for all laboratories. Alternatively, the referring physician provides the information about blood count (ideally, flow cytometry result) alongside the diagnosis and reason for referral, and the laboratory chooses the sample processing method based on tumor cell proportion and the limit of detection of the sequencing method. In that case, separation of mononuclear cells is satisfactory for most of cases at treatment initiation when the absolute lymphocyte count is usually high, while separation of CD19 + lymphocytes is performed only when the proportion of CLL cells in the sample is low (usually when ALC ≤ 10 × 10 9 /l, depending on the detection limit of the sequencing method and the aimed cut-off). If NGS with a low detection limit is used to detect variants in a sample with a low cancer fraction that has not been subjected to CD19 + cell enrichment, the VAF should be adjusted to the proportion of tumor cells.

We acknowledge that neither approach might be applicable in routine practice. When the laboratory does not receive the information on CLL cell content and routine CD19 + cell separation is not doable due to cost/time expenses, the laboratory should employ separation of mononuclear cells and inform the clinician in the report that the result should be interpreted with respect to tumor cells content in the provided sample.

In some circumstances, a lymph node or a bone marrow sample may also be used. In these cases, the content of tumor cells (typically in the pathology report) should be communicated between the clinic and the laboratory, and the knowledge is essential for the result interpretation.

NGS-based approaches for TP53 mutational analysis in CLL

Various commercial ready-to-use, custom, or entirely laboratory-developed approaches are used by different laboratories [ 66 ]. No specific methodology is recommended, and the laboratory is free to decide about the method based on resources and infrastructure (including computational resources), the focus of the laboratory (parallel analysis of other genes and diseases, minimal VAF to be detected), and legal requirements and reimbursement in the region [ 67 ]. In compliance with ISO 15189 standards for medical laboratories [ 68 ], all methods must be properly validated or verified (for details, see below). The EU-IVDR regulation [(EU) 2017/746] may increase the need for the use of commercial tests compliant with IVDR and the need for standardization of laboratory-developed tests.

The introduction of NGS methodology in the diagnostic routine is a complex process (Table  2 ); aspects to be considered are detailed e.g., in A Joint Consensus Recommendation of the Association for Molecular Pathology (AMP) and College of American Pathologists (CAP) [ 69 ] and in the guidelines issued by the Clinical and Laboratory Standards Institute (CLSI) [ 70 ]. Here, we summarize aspects that we consider worth highlighting specifically in the context of TP53 mutation analysis in CLL.

Library preparation and sequencing strategies

Targeted NGS can be used to analyze the TP53 gene as a standalone assay or as part of a gene panel investigating multiple genes. The method for detecting TP53 variants in CLL should be designed to detect low-VAF variants. We recommend to aim at least at 5% VAF; methods can be optimized to a 1% VAF or even <1% VAF. However, it is currently technically challenging to distinguish true variants from background noise at such a limit of detection [ 57 , 66 , 71 ]. To reliably detect low-VAF variants, sufficient DNA input must be used. The sample must contain an adequate number of variant molecules that should be distinguished from background noise. No strict recommendation regarding input DNA can be given. The laboratory should consider the aimed detection limit, number of required variant reads and the library conversion rate i.e. the percentage of input alleles that is present in the sample after library preparation that can be sequenced, which differs significantly among the library preparation methods (10–70%). As an example, if the laboratory aims at 20 supporting reads and a detection limit of 1% VAF, the minimum number of alleles to be sequenced is 2000. Providing that the library conversion rate is 40%, the number of input alleles should be at least 5000, i.e. 2500 cells, corresponding to 15 ng of DNA (a diploid genome of a human cell corresponds approximately to 6 pg of DNA). As there is variance in each step (dilution, pipetting, amplification, sequencing), we would recommend at least twice as high DNA input, i.e. 30 ng in this particular example.

For library preparation, both amplicon- and capture-based methods can be used, each having pros and cons. Amplicon methods can detect low-VAF variants efficiently but might be problematic regarding the quantification of variants and allele drop-out. When using hybrid capture NGS, the risk of allele drop-out is minimized, albeit library conversion rate may be less efficient. Single primer extension (SPE) has a good library conversion rate and represents an effective approach used by several companies. Capture methods and SPE are also easily extendable to other targets. For more accurate quantification and PCR and sequencing error correction, using unique molecular identifiers (UMI) is useful [ 72 ].

The sequencing technology is a quickly evolving field, and the currently used technologies employ different approaches, generating different error profiles. Further development in this field is expected to decrease the error rate for both short-read and long-read sequencing in the near future.

For reliable calling of low-VAF variants, sufficient sequencing coverage must be achieved. The desired coverage depth should be determined based on the intended limit of detection and the error rate of the whole assay (sample processing, library preparation, and sequencing). According to the binomial data distribution, a coverage depth of 250 unique reads for each position should be sufficient to detect 5% VAF with a threshold of variant supporting reads ≥5 [ 69 ]. We consider this as an absolute minimum for each position, and laboratories are encouraged to aim at higher coverage (>750), since 5 reads supporting the variant is mostly insufficient, and the minimum required number of variant reads varies among different methods. It is imperative to monitor the minimal coverage for each position within the TP53 coding region in each sequencing run. Importantly, this also pertains to the TP53 gene sequenced as a part of a gene panel. Median or mean coverage is not informative as some positions could be sequenced with lower-than-required coverage, thus contributing to the possibility of false-negative and false-positive results. The median coverage should usually be at least twice as high as the target minimal coverage, but this highly depends on the coverage uniformity. Laboratories might use an online calculator to help set the coverage [ 71 ], but the parameters should be verified in subsequent steps. Importantly, employing UMI for consensus variant calling requires significantly higher coverage as the number of reads is reduced during the analytical process.

Additionally, the laboratory may employ other methods to reliably call low-VAF variants, such as dilution-based approach [ 9 ], repeating the analysis, and error suppression bioinformatics [ 73 , 74 ].

Data analysis

The bioinformatics pipeline for NGS data analysis contains several steps, each of which can significantly influence the obtained results. Multiple commercial tools are available, some connected with the particular laboratory solution. Commercial tools are usually set to the safe, i.e., higher detection limit towards decreasing the risk of false positivity. Some of these tools allow changing the level of stringency; such change enables calling previously undetected variants but should be set with caution, and validated to prevent false-positive results. In-house bioinformatics pipelines are built based on multiple tools and can be adapted to individual needs, but they require an experienced bioinformatics team closely collaborating with the laboratory. Details of building and validation of in-house pipelines are out of the scope of this paper and can be found elsewhere [ 75 , 76 , 77 ].

The pipeline should provide an initial quality control summary including the coverage and other parameters, as it helps identify the samples with suboptimal results. The data generated by the bioinformatics pipeline should be carefully scrutinized focusing on technical artifacts that occur repeatedly within and among individual sequencing runs.

Validation/verification process

It is only acceptable to report laboratory results in clinical diagnostics after the method has been thoroughly validated or verified to ensure that the assay is suitable for its intended use, i.e., reliable detection of TP53 variants [ 68 , 69 ]. Commercially available CE-IVD/IVDR marked assays must be verified to confirm the manufacturer’s assay specifications using positive and negative controls with particular attention to the lowest VAF declared to be detected. Validation is a more detailed, multi-step process used for laboratory-developed, custom, and research-use-only (RUO) test, or CE-IVD assays used outside their designated range of use.

Certified reference material for thorough validation of somatic TP53 variants, especially if those of <10% VAF are considered, is, unfortunately, unavailable. As reference material, the following can be used: (i) DNA from young, healthy controls; (ii) DNA from cell lines carrying known TP53 variants (listed in the TP53 database ( https://TP53.isb-cgc.org/explore_cl ), which could be diluted to various VAFs; (iii) tumor DNA from patients analyzed with an orthogonal method.

The validation phase should be preceded by the optimization step, which involves performing a pilot run(s) with well-characterized reference samples. During this step, unanticipated problems with an NGS test are identified, and critical values are set that trigger close evaluation and warn about the unreliability of the result (Table  2 ).

The validation process of the NGS method must be documented and should consider all possible variables that may influence the performance of the assay (Table  2 ). In the context of validation, parameters describing the test performance should be assessed (Supplementary Table  S3 ). The terminology referring to the performance parameters was adopted from analytical chemistry and its transfer to NGS field resulted in inconsistency and confusion. Different meanings of the same term can be noted among clinical laboratories and also in various guidelines. This applies, in particular, for “limit of detection (LoD)”, “detection limit”, “sensitivity”, and “analytical sensitivity” that are sometimes used interchangeably, but are also used in several other ways (see the Clinical and Laboratory Standard Institute Harmonized Terminology Database: https://clsi.org/standards-development/harmonized-terminology-database/ ). Therefore, it is always recommended to include a brief explanation of the used term in the report. Here, we adopted the terminology and definitions according the Clinical and Laboratory Standard Institute [ 70 ].

As a first step, the background of the method must be assessed based on sequencing of DNA from young healthy controls. Based on the background distribution, the value that enables distinguishing true variants from background is set, usually referred as to Limit of Blank (LoB). Background noise is variant- and method-specific and consists of errors that may arise in each step of the sequencing process, i.e. library preparation, sequencing and bioinformatics processing. Also, background may be influenced by multiplexing of libraries of variable complexity due to index mis-assignment (index swap). It is generally low in non-patterned bridge-amplification platforms but still may affect ultra-sensitive approaches [ 78 , 79 ]. Effect of index swap can be minimized by using unique dual indexing (UDI).

As a next step, the minimum allele fraction that can be confidently detected should be evaluated using serially diluted variant-positive samples (optimally, patient samples with known variants should be used). This value is referred as to limit of detection (LoD) and is set based on the required confidence with respect to false-positive and false-negative result probabilities. The greater the distance between LoD and LoB is, the higher the confidence is that the variant is true; on the other hand, the probability of false-negative result increases. Either the overall LoD of the whole assay is estimated (e.g. ensuring truly calling of 99% of all variants), or a variant-specific LoD is set (an approach used by most research studies [ 4 , 5 , 6 , 9 ]). Assessing LoD and LoB is particularly challenging in the case of TP53 assessment as the variants can occur in nearly any nucleotide position of TP53 gene and it is virtually impossible to test all of the potentially existing pathogenic variants at various VAFs; this is even more complicated for variants other than SNVs – e.g., short insertions/deletions. Therefore, the LoD represents only an estimation, and the higher the number of tested variants is, the more precise the estimation is. The set of tested variants should include not only missense variants, but also deletions and insertions, ideally in different gene positions.

Other parameters to be described involve repeatability, reproducibility, and wide range of predictive values. For details, see Supplementary Table  S3 and refer to special literature [ 69 , 70 , 80 , 81 ].

Continuous monitoring of quality

The performance of the method should be continuously monitored in clinical routine diagnostics. The error rate of each run and sample should be checked. It is recommended to run the same samples repeatedly over an extended period [ 69 ] and to perform periodic analyses of reference samples. It is advisable to record all the obtained results in an internal database. It enables following the presence of variants in consecutive samples of individual patients and monitoring the concordance of the obtained results with published data and databases. Repeatedly observed atypical results might suggest an erroneous workflow. Specifically, attention should be paid to the frequency and mutual association of TP53 mutations and 17p deletions, frequency of low-burden mutations, and TP53 mutation profile, which is similar to other cancers with a very few exceptions, such as a high prevalence of variant c.626_627del p.Arg209Lysfs in CLL [ 82 ] (Fig.  4 ).

TP53 variant profile based on data collected for CLL patients in the UMD database; common polymorphisms have been omitted [ 82 ]. A Codon distribution with hot-spot variants depicted. Variants in codons 175, 248, and 273 are general hot spots, while the truncating frameshift variant in codon 209 is CLL-specific. B Exon distribution showing the prevalence of variants in exons 5–8. C Proportion of variant types out of all variants. D Proportion of variant types in individual domains. In the DNA-binding domain, missense variants prevail; conversely, truncating variants are predominant in the carboxy and amino termini.

Regular participation in external quality assessment should be standard and is required by ISO 15189. For instance, ERIC cooperates with GenQA/UK NEQAS-LI to assure the quality of TP53 testing in patients with CLL: ERIC TP53 Certification ensures the initial control of the method implementation, including the detection of low-VAF TP53 variants ( http://www.ericll.org/ ), while GenQA/UK NEQAS-LI supports the continuous quality check. Furthermore, ERIC has assisted with interlaboratory comparison of low-VAF variants [ 66 ] and will further support such activities.

Interpretation of the results and reporting

Variant description.

Detected variants must be described using the nomenclature devised by the Human Genome Variation Society (HGVS) [ 83 ]. Software tools are helpful to ensure adherence to standardized nomenclature: Mutalyzer [ 84 ], or TP53 -specific tool Seshat, with Mutalyzer embedded [ 85 ].

Attention must be paid to the mRNA transcript provided by the bioinformatics pipeline. The preferred reference sequence is the transcript suggested by the MANE project (Ensembl or NCBI) [ 86 ] as new Locus Reference Genomic sequences (LRGs) are no longer generated.

Terminology note – the term “variant” is the only acceptable designation in the germline context. For somatic variants, the term “mutation” can be used [ 83 ]. From the molecular point of view, somatically gained variants are true “mutations.” Even though the somatic origin is not proven in tumor-only mode, the vast majority of the TP53 variants found in patients with CLL are truly somatic. Therefore, using the term “mutation” is acceptable for the sake of simplification in clinical utilization in CLL.

Variant interpretation

Variant interpretation is an integral part of cancer diagnostics. Several consortia have published guidelines for the classification of variants addressing their functional impact and clinical implications (Supplementary Table  S4 ). For germline variants, A joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) [ 87 ] became a standard for classification into five pathogenicity classes. Expert panels for specific genes/diseases further refine these guidelines by providing recommendations for particular genes/diseases (e.g., ClinGen Expert Panel for TP53 [ 88 ]). For somatic variants, distinct classification systems have been published with the aim of defining pathogenicity [ 89 ], oncogenicity [ 90 ], clinical significance [ 91 ] or clinical actionability [ 92 ], and modified versions have been issued by national societies [ 93 ]. As a result, this situation might cause confusion, and no standardization regarding variant classification and terminology currently exists. Regardless of the classification system applied, it is necessary to adhere to the terminology of the classification system mentioned in the report.

To assist variant interpretation, a plethora of ever-evolving databases, in silico predictors, and aggregation tools are available, many of them designed to be embedded in the bioinformatics pipelines for NGS data analysis (reviewed in [ 94 ]). Data obtained through the use of these general tools can assist with the classification of variants detected in larger sets of genes but are often insufficient, or even incorrect. Especially, in-silico tools do not work well in the case of TP53 variants. Moreover, submissions may not be subject to a level of curation sufficient for clinical diagnostic application e.g., different pathogenic TP53 variants are falsely included in dbSNP databases.

For the purposes of TP53 analysis in CLL, ERIC standards require using TP53 -specific databases (see details below) with the support of tools listed in Supplementary Table  S5 . Overall, we believe that the interpretation workflow might be significantly simplified for the following reasons: (i) TP53 is the most studied tumor suppressor gene and detailed functional data on transactivation ability [ 95 ], loss of growth suppression [ 96 , 97 ], and dominant negative effect [ 96 ] are available for virtually all missense TP53 variants. These data from large-scale studies are easily accessible via TP53 -specific databases: the TP53 database ( https://TP53.isb-cgc.org/ originally IARC database) [ 98 ], and, the TP53 website ( https://p53.fr/ ) [ 99 ] with the tool Seshat [ 85 ]; (ii) from the point of clinical significance and actionability, all somatic TP53 variants impairing function, i.e. (likely) pathogenic/oncogenic variants, found in patients with CLL are assigned to Tier I - Variants of Strong Clinical Significance [ 91 ], and Target suitable for routine use [ 92 ]; (iii) the vast majority of TP53 variants detected in CLL are pathogenic or likely pathogenic [ 82 ] and the difference between these two categories does not impact on clinical decision-making in patients with CLL; (iv) when deciding about the oncogenicity/pathogenicity of difficult-to-interpret variants, evidence from hereditary cancer syndromes might be applied [ 90 ]. Any germline variant proven to be pathogenic or benign according to the “germline” criteria can be interpreted accordingly when seen as somatic. In this respect, ClinGen TP53 Variant Curation Expert Panel specifications [ 88 ] and the ClinGen Evidence Repository of curated variants ( https://erepo.clinicalgenome.org/evrepo/ui/classifications?matchMode=exact&gene=TP53 ) are assistive.

On these grounds, ERIC proposes for CLL a simplified classification algorithm in which null variants and variants with concordant results from functional studies [ 95 , 96 , 97 ] could be classified right away as pathogenic/oncogenic without complicated and time-consuming specification of the criteria (Fig.  5 with more details in Supplementary Figure  1 and notes and clarifications in Supplementary Table  S6 ). This covers most somatic TP53 variants found in CLL in routine practice. A more detailed evaluation of the oncogenicity/pathogenicity is required only for a minority of the variants (Fig.  6A ). Variants with preserved functionality, i.e., (likely) benign variants are infrequent in the somatic context in CLL (Fig.  6B ), and such finding is indicative of either germline origin or technical artifact. However, we cannot entirely exclude the presence of a passenger functional TP53 variant or rare cases of variants of unknown significance. We must admit that p53 functions in the cell are highly complex, therefore, the effects of individual missense mutations are context-dependent [ 97 ]. Nevertheless, we believe that a certain degree of simplification is necessary for the purposes of routine CLL diagnostics.

A classification algorithm showing the basic principles of assigning variants into pathogenicity/oncogenicity classes. A detailed version of the algorithm listing assistive tools and specific variants classified into respective categories can be found in Supplementary Figure 1. Databases instrumental in the interpretation of TP53 variants are listed in Supplementary Table  S5 . # Might be misclassified as synonymous or missense and listed as such in some databases. *Oncogenicity classification according to Horak et al. [ 90 ] is also acceptable. Occurrence according to the UMD database [ 82 ]. VUS variant of unknown significance.

Illustrative example based on data published in Malcikova et al. [ 6 ]. Common population variants have been excluded. A Breakdown based on assignment using proposed classification algorithm (color coding corresponds to Fig.  5 ). Concordant functional/non-functional: assessed by functional tests (Kato et al. [ 95 ], Giacomelli et al. [ 96 ] and Kotler et al. [ 97 ]). B Proportion of TP53 variants detected in CLL assigned to pathogenicity categories. VUS variant of unknown significance.

Non-tumor DNA testing

CLL is a late-onset cancer not belonging to the Li-Fraumeni syndrome tumor spectrum, and the probability that the detected pathogenic variant in the TP53 gene is of germline origin is extremely low. Thus, a test to confirm/exclude somatic origin is not generally recommended [ 100 ], even for variants with VAF ≥ 50%, as this is a common finding in CLL. In very rare cases, germline origin of (likely) pathogenic variants might be suspected based on clinical information (e.g., presence of family/personal history of Li-Fraumeni-associated cancer and/or exceptionally young age of CLL onset - <40 years); in this case, testing of non-tumor DNA might be considered. In case of suspicion, the patient should be referred to a clinical geneticist before reaching any conclusion on hereditary cancer syndrome testing [ 101 ]. Confirming the germline origin must conclude a thorough review of pathogenicity, as a pathogenic variant has far-reaching consequences for the patient and their family.

If indicated, testing of germline origin in patients with CLL should be performed from a non-tumor sample. Given the challenge of obtaining cultured skin fibroblast - the gold standard for germline testing in hereditary hematopoietic malignancies [ 102 ] - using an alternative material is acceptable. This can be one of: sorted T cells/CD19-negative fraction (absence of leukemic cells confirmed by flow cytometry), remission samples, buccal swabs/saliva, or other tissues according to the local policy. However, it is essential to keep in mind that also putative tumor-free material (i.e. saliva or CD19 negative blood cells) can be contaminated by CLL cells [ 103 ], active myeloid malignancy precursors (e.g., therapy-related myelodysplastic syndrome [ 104 ] or myeloproliferative neoplasm) or clonal hematopoiesis of indeterminate potential. Allelic frequency of >30% (SNVs) or >20% (small insertions/deletions) in non-tumor tissue is expected for variants of germline origin [ 100 ], and lower VAFs are indicative of cancer cell contamination or, rarely, mosaicism. When the germline origin of the pathogenic TP53 variant is suspected based on non-tumor sequencing, it is advisable to confirm the result from independent tissue, according to the guidelines for testing in hematopoietic malignancies [ 102 ].

The report should be concise and straightforward, while at the same time including all available information that could be relevant to the referring clinician. The obligatory information is summarized in an update of the European Society of Human Genetics (ESHG) recommendations for reporting the results of diagnostic genetic testing [ 105 ]. Reports should adhere to the international standard ISO 15189 [ 68 ] with the specifications formulated by national accreditation bodies. The template form is provided as  Supplementary material but check for the most updated version on www.ericll.org .

Important points to consider when creating a report include the following:

The cell separation method must be specified in the report. If CD19 + cell separation has not been performed, we recommend to include a statement that the result should be interpreted with respect to the proportion of tumor cells in the sample and the separation method used, as a low proportion of tumor cells may lead to a false-negative result or a decreased VAF.

A clear and brief description of the method and its limitations should be provided, e.g., most sequencing methods are not designed to detect long insertions and deletions spanning whole exons or introns.

The lowest VAFs that can be reliably detected should be indicated to inform the clinician at which cut-off level the majority of variants is called. This information is essential particularly when issuing negative results.

Coverage of the whole coding region must be reported (≥99% minimum coverage). Since the TP53 gene is short and easily covered, covering all bases in the coding region with a sufficient number of reads should be a standard.

Estimating allele status based on VAF should be avoided (50% VAF can be heterozygous, hemizygous, or homozygous depending on cancer cell fraction and separation method). Also, the VAF does not equal the number of affected cells.

A brief conclusion summarizing the possible prognostic impact or resistance is recommended to be included in the report along with a reference to the corresponding literature. The content of this conclusion should follow national policies as differences exist between countries regarding the responsibility of the laboratory and the clinician.

Due to the very low probability of finding a (likely) pathogenic TP53 variant of germline origin, it is discouraged to suggest in the report the possibility of Li-Fraumeni or other cancer hereditary syndrome (see section “Interpretation, Non-tumor DNA testing”). We recommend mentioning the fact that “the method cannot distinguish between somatic and germline variants” among method limitations.

Chemoimmunotherapy (CIT) is no longer an option for patients with a TP53 aberration, irrespective of the clone size. Treatment with targeted agents might prevent the undesirable expansion of TP53 -mutated clones accompanied by the evolution of other aberrations (e.g. complex karyotype). Nevertheless, data on TP53 mutations is still evolving in the targeted agent setting and the evidence is not yet mature enough to guide treatment choices among targeted agents (e.g. BTKi and BCL2i) or regimens. ERIC emphasizes the importance of precise classification of TP53 aberrations (del(17p) vs. TP53 mutation, mono- vs. biallelic aberrations), as well as inclusion low-VAF TP53 variants in the design of clinical trials in order to obtain robust evidence for improving the treatment tailoring.

We recommend reporting all TP53 variants above the LoD set by the laboratory. We emphasize the need for method validation or verification to provide a reliable result, especially in the case of low-VAF variants. It is important for the diagnostic laboratories to adhere to ISO standards. Regarding variant interpretation, most TP53 variants detected in CLL are unambiguously pathogenic but, in a few instances, the interpretation is less straightforward. We summarized the available information into an algorithm in which the majority of TP53 variants are classified directly, and we here provide a guide for the interpretation of the less common ambiguous variants. ERIC will continue educational and harmonizing efforts to facilitate robust TP53 assessment in CLL by organizing educational seminars and QC initiatives and operating an ERIC TP53 helpdesk for laboratories seeking assistance available at www.ericll.org .

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Supported by: Conceptual development of research organization (FNBr 65269705) provided by the Ministry of Health of the Czech Republic, the project National Institute for Cancer Research (Programme EXCELES, ID Project No. LX22NPO5102) - Funded by the European Union - Next Generation EU. The Swedish Cancer Society the Swedish Research Council, Region Stockholm and Radiumhemmets Forskningsfonder, Stockholm.

Author information

These authors contributed equally: Jitka Malcikova, Sarka Pavlova.

These authors jointly supervised this work: Paolo Ghia, Sarka Pospisilova

Authors and Affiliations

Department of Internal Medicine, Hematology and Oncology, and Institute of Medical Genetics and Genomics, University Hospital Brno and Medical Faculty, Masaryk University, Brno, Czech Republic

Jitka Malcikova, Sarka Pavlova & Sarka Pospisilova

Central European Institute of Technology, Masaryk University, Brno, Czech Republic

Jitka Malcikova, Sarka Pavlova, Boris Tichy & Sarka Pospisilova

Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden

Panagiotis Baliakas & Thierry Soussi

Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece

Thomas Chatzikonstantinou & Kostas Stamatopoulos

Division of CLL, Department of Internal Medicine III, Ulm University, Ulm, Germany

Eugen Tausch & Stephan Stilgenbauer

Haematology Department, Belfast Health and Social Care Trust, Belfast, United Kingdom

Mark Catherwood

Hematology, Oncology Institute of Southern Switzerland and Institute of Oncology Research, Università della Svizzera Italiana, Bellinzona, Switzerland

Davide Rossi

Hematopoietic and Leukemic Development, UMRS_938, Sorbonne University, Paris, France

Thierry Soussi

Department of Hematology, Cancer Center Amsterdam, Amsterdam University Medical Centers, Amsterdam, the Netherlands

Arnon P. Kater

Department of Hematology, Rigshospitalet, Copenhagen, Denmark

Carsten U. Niemann

Sorbonne Université, Paris, France

Frederic Davi

Department of Hematology, Hôpital Pitié-Salpêtière, AP-HP, Paris, France

Division of Haematology, Department of Translational Medicine, University of Eastern Piedmont, Novara, Italy

Gianluca Gaidano

Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden

Richard Rosenquist

Clinical Genetics and Genomics, Karolinska University Hospital, Stockholm, Sweden

Università Vita-Salute San Raffaele, Milan, Italy

Strategic Research Program on CLL, Division of Experimental Oncology, IRCCS Ospedale San Raffaele, Milan, Italy

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Contributions

All authors contributed to the article, critically evaluated the content, and approved the submitted version. JM, SPav, PB, TC and BT wrote the manuscript., ET, MC, DR, TS, AK, CUN, FD, GG, SS, RR and KS edited the text, PG and SPosp coordinated manuscript preparation, edited the text and gave final approval.

Corresponding authors

Correspondence to Paolo Ghia or Sarka Pospisilova .

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SPav has received honoraria from AstraZeneca. PB has received honoraria from Abbvie, Gilead and Janssen, and research funding from Gilead. ET has received honoraria from Abbvie, AstraZeneca, BeiGene, Janssen and Hoffmann-La Roche, and research support from Abbvie, Roche and Gilead. DR has received honoraria from AbbVie, AstraZeneca, BeiGene, BMS, Janssen and Lilly, research grants from AbbVie, AstraZeneca and Janssen, and travel grants from AstraZeneca and Janssen. AK has received research funding from BMS, Astra Zeneca, Janssen, Abbvie and Roche Genentech, and compensation as a member of the scientific advisory board from BMS, Astra Zeneca, Janssen, Abbvie, Roche Genentec and LAVA. CUN has received research funding and/or consultancy fees from Abbvie, AstraZeneca, Beigene, Janssen, Genmab, Lilly, MSD, CSL Behring, Takeda and Octapharma. FD has received honoraria from Janssen and AstraZeneca. GG has received compensation as a member of the scientific advisory board from Abbvie, Astra Zeneca, BeiGene, Incyte, Janssen and Lilly, and Speaker’s Bureau honoraria from Abbvie, BeiGene, Astra Zeneca and Janssen. SS has received compensation as a member of the scientific advisory board, research support, travel support and speaker fees from AbbVie, Acerta, Amgen, AstraZeneca, BeiGene, BMS, Celgene, Gilead, GSK, Hoffmann-La Roche, Infinity, Janssen, Lilly, Novartis, Sunesis, and Verastem. RR has received honoraria from AbbVie, AstraZeneca, Janssen, Illumina, and Roche. KS has received research funding, honoraria and/or consultancy fees from Abbvie, AstraZeneca, Janssen, Lilly and Roche. PG has received honoraria from AbbVie, Astrazeneca, BeiGene, BMS, Galapagos, Janssen, Lilly/Loxo Oncology, MSD and Roche, and research funding from AbbVie, Astrazeneca, BMS and Janssen.

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Malcikova, J., Pavlova, S., Baliakas, P. et al. ERIC recommendations for TP53 mutation analysis in chronic lymphocytic leukemia—2024 update. Leukemia (2024). https://doi.org/10.1038/s41375-024-02267-x

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DOI : https://doi.org/10.1038/s41375-024-02267-x

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Brokers Suggest Investing in Builders FirstSource (BLDR): Read This Before Placing a Bet

The recommendations of Wall Street analysts are often relied on by investors when deciding whether to buy, sell, or hold a stock. Media reports about these brokerage-firm-employed (or sell-side) analysts changing their ratings often affect a stock's price. Do they really matter, though?

Before we discuss the reliability of brokerage recommendations and how to use them to your advantage, let's see what these Wall Street heavyweights think about Builders FirstSource ( BLDR Quick Quote BLDR - Free Report ) .

Builders FirstSource currently has an average brokerage recommendation (ABR) of 1.56, on a scale of 1 to 5 (Strong Buy to Strong Sell), calculated based on the actual recommendations (Buy, Hold, Sell, etc.) made by 16 brokerage firms. An ABR of 1.56 approximates between Strong Buy and Buy.

Of the 16 recommendations that derive the current ABR, 11 are Strong Buy and one is Buy. Strong Buy and Buy respectively account for 68.8% and 6.3% of all recommendations.

Brokerage Recommendation Trends for BLDR

This means that the interests of these institutions are not always aligned with those of retail investors, giving little insight into the direction of a stock's future price movement. It would therefore be best to use this information to validate your own analysis or a tool that has proven to be highly effective at predicting stock price movements.

With an impressive externally audited track record, our proprietary stock rating tool, the Zacks Rank, which classifies stocks into five groups, ranging from Zacks Rank #1 (Strong Buy) to Zacks Rank #5 (Strong Sell), is a reliable indicator of a stock's near -term price performance. So, validating the Zacks Rank with ABR could go a long way in making a profitable investment decision.

Zacks Rank Should Not Be Confused With ABR

Although both Zacks Rank and ABR are displayed in a range of 1-5, they are different measures altogether.

Broker recommendations are the sole basis for calculating the ABR, which is typically displayed in decimals (such as 1.28). The Zacks Rank, on the other hand, is a quantitative model designed to harness the power of earnings estimate revisions. It is displayed in whole numbers -- 1 to 5.

It has been and continues to be the case that analysts employed by brokerage firms are overly optimistic with their recommendations. Because of their employers' vested interests, these analysts issue more favorable ratings than their research would support, misguiding investors far more often than helping them.

On the other hand, earnings estimate revisions are at the core of the Zacks Rank. And empirical research shows a strong correlation between trends in earnings estimate revisions and near-term stock price movements.

Furthermore, the different grades of the Zacks Rank are applied proportionately across all stocks for which brokerage analysts provide earnings estimates for the current year. In other words, at all times, this tool maintains a balance among the five ranks it assigns.

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Is BLDR Worth Investing In?

Looking at the earnings estimate revisions for Builders FirstSource, the Zacks Consensus Estimate for the current year has declined 5.2% over the past month to $13.12.

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Artificial intelligence in strategy

Can machines automate strategy development? The short answer is no. However, there are numerous aspects of strategists’ work where AI and advanced analytics tools can already bring enormous value. Yuval Atsmon is a senior partner who leads the new McKinsey Center for Strategy Innovation, which studies ways new technologies can augment the timeless principles of strategy. In this episode of the Inside the Strategy Room podcast, he explains how artificial intelligence is already transforming strategy and what’s on the horizon. This is an edited transcript of the discussion. For more conversations on the strategy issues that matter, follow the series on your preferred podcast platform .

Joanna Pachner: What does artificial intelligence mean in the context of strategy?

Yuval Atsmon: When people talk about artificial intelligence, they include everything to do with analytics, automation, and data analysis. Marvin Minsky, the pioneer of artificial intelligence research in the 1960s, talked about AI as a “suitcase word”—a term into which you can stuff whatever you want—and that still seems to be the case. We are comfortable with that because we think companies should use all the capabilities of more traditional analysis while increasing automation in strategy that can free up management or analyst time and, gradually, introducing tools that can augment human thinking.

Joanna Pachner: AI has been embraced by many business functions, but strategy seems to be largely immune to its charms. Why do you think that is?

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Yuval Atsmon: You’re right about the limited adoption. Only 7 percent of respondents to our survey about the use of AI say they use it in strategy or even financial planning, whereas in areas like marketing, supply chain, and service operations, it’s 25 or 30 percent. One reason adoption is lagging is that strategy is one of the most integrative conceptual practices. When executives think about strategy automation, many are looking too far ahead—at AI capabilities that would decide, in place of the business leader, what the right strategy is. They are missing opportunities to use AI in the building blocks of strategy that could significantly improve outcomes.

I like to use the analogy to virtual assistants. Many of us use Alexa or Siri but very few people use these tools to do more than dictate a text message or shut off the lights. We don’t feel comfortable with the technology’s ability to understand the context in more sophisticated applications. AI in strategy is similar: it’s hard for AI to know everything an executive knows, but it can help executives with certain tasks.

When executives think about strategy automation, many are looking too far ahead—at AI deciding the right strategy. They are missing opportunities to use AI in the building blocks of strategy.

Joanna Pachner: What kind of tasks can AI help strategists execute today?

Yuval Atsmon: We talk about six stages of AI development. The earliest is simple analytics, which we refer to as descriptive intelligence. Companies use dashboards for competitive analysis or to study performance in different parts of the business that are automatically updated. Some have interactive capabilities for refinement and testing.

The second level is diagnostic intelligence, which is the ability to look backward at the business and understand root causes and drivers of performance. The level after that is predictive intelligence: being able to anticipate certain scenarios or options and the value of things in the future based on momentum from the past as well as signals picked in the market. Both diagnostics and prediction are areas that AI can greatly improve today. The tools can augment executives’ analysis and become areas where you develop capabilities. For example, on diagnostic intelligence, you can organize your portfolio into segments to understand granularly where performance is coming from and do it in a much more continuous way than analysts could. You can try 20 different ways in an hour versus deploying one hundred analysts to tackle the problem.

Predictive AI is both more difficult and more risky. Executives shouldn’t fully rely on predictive AI, but it provides another systematic viewpoint in the room. Because strategic decisions have significant consequences, a key consideration is to use AI transparently in the sense of understanding why it is making a certain prediction and what extrapolations it is making from which information. You can then assess if you trust the prediction or not. You can even use AI to track the evolution of the assumptions for that prediction.

Those are the levels available today. The next three levels will take time to develop. There are some early examples of AI advising actions for executives’ consideration that would be value-creating based on the analysis. From there, you go to delegating certain decision authority to AI, with constraints and supervision. Eventually, there is the point where fully autonomous AI analyzes and decides with no human interaction.

Because strategic decisions have significant consequences, you need to understand why AI is making a certain prediction and what extrapolations it’s making from which information.

Joanna Pachner: What kind of businesses or industries could gain the greatest benefits from embracing AI at its current level of sophistication?

Yuval Atsmon: Every business probably has some opportunity to use AI more than it does today. The first thing to look at is the availability of data. Do you have performance data that can be organized in a systematic way? Companies that have deep data on their portfolios down to business line, SKU, inventory, and raw ingredients have the biggest opportunities to use machines to gain granular insights that humans could not.

Companies whose strategies rely on a few big decisions with limited data would get less from AI. Likewise, those facing a lot of volatility and vulnerability to external events would benefit less than companies with controlled and systematic portfolios, although they could deploy AI to better predict those external events and identify what they can and cannot control.

Third, the velocity of decisions matters. Most companies develop strategies every three to five years, which then become annual budgets. If you think about strategy in that way, the role of AI is relatively limited other than potentially accelerating analyses that are inputs into the strategy. However, some companies regularly revisit big decisions they made based on assumptions about the world that may have since changed, affecting the projected ROI of initiatives. Such shifts would affect how you deploy talent and executive time, how you spend money and focus sales efforts, and AI can be valuable in guiding that. The value of AI is even bigger when you can make decisions close to the time of deploying resources, because AI can signal that your previous assumptions have changed from when you made your plan.

Joanna Pachner: Can you provide any examples of companies employing AI to address specific strategic challenges?

Yuval Atsmon: Some of the most innovative users of AI, not coincidentally, are AI- and digital-native companies. Some of these companies have seen massive benefits from AI and have increased its usage in other areas of the business. One mobility player adjusts its financial planning based on pricing patterns it observes in the market. Its business has relatively high flexibility to demand but less so to supply, so the company uses AI to continuously signal back when pricing dynamics are trending in a way that would affect profitability or where demand is rising. This allows the company to quickly react to create more capacity because its profitability is highly sensitive to keeping demand and supply in equilibrium.

Joanna Pachner: Given how quickly things change today, doesn’t AI seem to be more a tactical than a strategic tool, providing time-sensitive input on isolated elements of strategy?

Yuval Atsmon: It’s interesting that you make the distinction between strategic and tactical. Of course, every decision can be broken down into smaller ones, and where AI can be affordably used in strategy today is for building blocks of the strategy. It might feel tactical, but it can make a massive difference. One of the world’s leading investment firms, for example, has started to use AI to scan for certain patterns rather than scanning individual companies directly. AI looks for consumer mobile usage that suggests a company’s technology is catching on quickly, giving the firm an opportunity to invest in that company before others do. That created a significant strategic edge for them, even though the tool itself may be relatively tactical.

Joanna Pachner: McKinsey has written a lot about cognitive biases  and social dynamics that can skew decision making. Can AI help with these challenges?

Yuval Atsmon: When we talk to executives about using AI in strategy development, the first reaction we get is, “Those are really big decisions; what if AI gets them wrong?” The first answer is that humans also get them wrong—a lot. [Amos] Tversky, [Daniel] Kahneman, and others have proven that some of those errors are systemic, observable, and predictable. The first thing AI can do is spot situations likely to give rise to biases. For example, imagine that AI is listening in on a strategy session where the CEO proposes something and everyone says “Aye” without debate and discussion. AI could inform the room, “We might have a sunflower bias here,” which could trigger more conversation and remind the CEO that it’s in their own interest to encourage some devil’s advocacy.

We also often see confirmation bias, where people focus their analysis on proving the wisdom of what they already want to do, as opposed to looking for a fact-based reality. Just having AI perform a default analysis that doesn’t aim to satisfy the boss is useful, and the team can then try to understand why that is different than the management hypothesis, triggering a much richer debate.

In terms of social dynamics, agency problems can create conflicts of interest. Every business unit [BU] leader thinks that their BU should get the most resources and will deliver the most value, or at least they feel they should advocate for their business. AI provides a neutral way based on systematic data to manage those debates. It’s also useful for executives with decision authority, since we all know that short-term pressures and the need to make the quarterly and annual numbers lead people to make different decisions on the 31st of December than they do on January 1st or October 1st. Like the story of Ulysses and the sirens, you can use AI to remind you that you wanted something different three months earlier. The CEO still decides; AI can just provide that extra nudge.

Joanna Pachner: It’s like you have Spock next to you, who is dispassionate and purely analytical.

Yuval Atsmon: That is not a bad analogy—for Star Trek fans anyway.

Joanna Pachner: Do you have a favorite application of AI in strategy?

Yuval Atsmon: I have worked a lot on resource allocation, and one of the challenges, which we call the hockey stick phenomenon, is that executives are always overly optimistic about what will happen. They know that resource allocation will inevitably be defined by what you believe about the future, not necessarily by past performance. AI can provide an objective prediction of performance starting from a default momentum case: based on everything that happened in the past and some indicators about the future, what is the forecast of performance if we do nothing? This is before we say, “But I will hire these people and develop this new product and improve my marketing”— things that every executive thinks will help them overdeliver relative to the past. The neutral momentum case, which AI can calculate in a cold, Spock-like manner, can change the dynamics of the resource allocation discussion. It’s a form of predictive intelligence accessible today and while it’s not meant to be definitive, it provides a basis for better decisions.

Joanna Pachner: Do you see access to technology talent as one of the obstacles to the adoption of AI in strategy, especially at large companies?

Yuval Atsmon: I would make a distinction. If you mean machine-learning and data science talent or software engineers who build the digital tools, they are definitely not easy to get. However, companies can increasingly use platforms that provide access to AI tools and require less from individual companies. Also, this domain of strategy is exciting—it’s cutting-edge, so it’s probably easier to get technology talent for that than it might be for manufacturing work.

The bigger challenge, ironically, is finding strategists or people with business expertise to contribute to the effort. You will not solve strategy problems with AI without the involvement of people who understand the customer experience and what you are trying to achieve. Those who know best, like senior executives, don’t have time to be product managers for the AI team. An even bigger constraint is that, in some cases, you are asking people to get involved in an initiative that may make their jobs less important. There could be plenty of opportunities for incorpo­rating AI into existing jobs, but it’s something companies need to reflect on. The best approach may be to create a digital factory where a different team tests and builds AI applications, with oversight from senior stakeholders.

The big challenge is finding strategists to contribute to the AI effort. You are asking people to get involved in an initiative that may make their jobs less important.

Joanna Pachner: Do you think this worry about job security and the potential that AI will automate strategy is realistic?

Yuval Atsmon: The question of whether AI will replace human judgment and put humanity out of its job is a big one that I would leave for other experts.

The pertinent question is shorter-term automation. Because of its complexity, strategy would be one of the later domains to be affected by automation, but we are seeing it in many other domains. However, the trend for more than two hundred years has been that automation creates new jobs, although ones requiring different skills. That doesn’t take away the fear some people have of a machine exposing their mistakes or doing their job better than they do it.

Joanna Pachner: We recently published an article about strategic courage in an age of volatility  that talked about three types of edge business leaders need to develop. One of them is an edge in insights. Do you think AI has a role to play in furnishing a proprietary insight edge?

Yuval Atsmon: One of the challenges most strategists face is the overwhelming complexity of the world we operate in—the number of unknowns, the information overload. At one level, it may seem that AI will provide another layer of complexity. In reality, it can be a sharp knife that cuts through some of the clutter. The question to ask is, Can AI simplify my life by giving me sharper, more timely insights more easily?

Joanna Pachner: You have been working in strategy for a long time. What sparked your interest in exploring this intersection of strategy and new technology?

Yuval Atsmon: I have always been intrigued by things at the boundaries of what seems possible. Science fiction writer Arthur C. Clarke’s second law is that to discover the limits of the possible, you have to venture a little past them into the impossible, and I find that particularly alluring in this arena.

AI in strategy is in very nascent stages but could be very consequential for companies and for the profession. For a top executive, strategic decisions are the biggest way to influence the business, other than maybe building the top team, and it is amazing how little technology is leveraged in that process today. It’s conceivable that competitive advantage will increasingly rest in having executives who know how to apply AI well. In some domains, like investment, that is already happening, and the difference in returns can be staggering. I find helping companies be part of that evolution very exciting.

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O’Hara R, Johnson M, Hirst E, et al. A qualitative study of decision-making and safety in ambulance service transitions. Southampton (UK): NIHR Journals Library; 2014 Dec. (Health Services and Delivery Research, No. 2.56.)

A qualitative study of decision-making and safety in ambulance service transitions.

Chapter 8 conclusions and recommendations.

The aim of this study was to explore the range and nature of influences on safety in decision-making by ambulance service staff (paramedics). A qualitative approach was adopted using a range of complementary methods. The study has provided insights on the types of decisions that staff engage in on a day-to-day basis. It has also identified a range of system risk factors influencing decisions about patient care. Although this was a relatively small-scale exploratory study, confidence in the generalisability of the headline findings is enhanced by the high level of consistency in the findings, obtained using multiple methods, and the notable consensus among participants.

The seven predominant system influences identified should not be considered discrete but as overlapping and complementary issues. They also embody a range of subthemes that represent topics for future research and/or intervention.

The apparently high level of consistency across the participating trusts suggests that the issues identified may be generic and relevant to other ambulance service trusts.

In view of the remit of this study, aspects relating to system weaknesses and potential threats to patient safety dominate in the account of findings. However, it should be noted that respondent accounts also provided examples of systems that were said to be working well, for example specific care management pathways, local roles and ways of working and technological initiatives such as IBIS and the ePRF.

• Implications for health care

The NHS system within which the ambulance service operates is characterised in our study as fragmented and inconsistent. For ambulance service staff the extent of variation across the geographical areas in which they work is problematic in terms of knowing what services are available and being able to access them. The lack of standardisation in practice guidelines, pathways and protocols across services and between areas makes it particularly challenging for staff to keep up to date with requirements in different parts of their own trust locations and when crossing trust boundaries. Although a degree of consistency across the network is likely to improve the situation, it is also desirable to have sufficient flexibility to accommodate the needs of specific local populations. There was some concern over the potential for further fragmentation with the increased number of CCGs.

Ambulance services are increasingly under pressure to focus on reducing conveyance rates to A&E; this arguably intensifies the need to ensure that crews are appropriately skilled to be able to make effective decisions over the need to convey or not to convey if associated risks to patients are to be minimised. Our findings highlight the challenges of developing staff and ensuring that their skills are utilised where they are most needed within the context of organisational resource constraints and operational demands. Decisions over non-conveyance to A&E are moderated by the availability of alternative care pathways and providers. There were widespread claims of local variability in this respect. Staff training and development, and access to alternatives to A&E, were identified as priorities for attention by workshop attendees.

One of the difficulties for ambulance services is that they operate as a 24/7 service within a wider urgent and emergency care network that, beyond A&E, operates a more restricted working day. The study findings identify this as problematic for two reasons. First, it fuels demand for ambulance service care as a route to timely treatment, when alternatives may involve delay. Second, it contributes to inappropriate conveyance to A&E because more appropriate options are unavailable or limited during out-of-hours periods. Ultimately, this restricts the scope for ensuring that patients are getting the right level of care at the right time and place. Study participants identified some patient populations as particularly poorly served in terms of alternatives to A&E (e.g. those with mental health issues, those at the end of life, older patients and those with chronic conditions).

The effectiveness of the paramedic role in facilitating access to appropriate care pathways hinges on relationships with other care providers (e.g. primary care, acute care, mental health care, community health care). An important element relates to the cultural profile of paramedics in the NHS, specifically, the extent to which other health professionals and care providers consider the clinical judgements/decisions made by paramedics as credible and actionable. Staff identified this as a barrier to access where the ambulance service is still viewed primarily as a transport service. Consideration could be given to ways of improving effective teamworking and communication across service and professional boundaries.

Although paramedics acknowledged the difficulties of telephone triage, they also identified how the limitations of this system impact on them. Over-triage at the initial call-handling stage places considerable demands on both staff and vehicle resources. A related concern is the limited information conveyed to crews following triage. Initial triage was suggested as an area that warrants attention to improve resource allocation.

The findings highlight the challenges faced by front-line ambulance service staff. It was apparent that the extent and nature of the demand for ambulance conveyance represents a notable source of strain and tension for individuals and at an organisational level. For example, there were widespread claims that meeting operational demands for ambulance services limits the time available for training and professional development, with this potentially representing a risk for patients and for staff. Staff perceptions of risk relating to patient safety extend to issues of secondary risk management, that is, personal and institutional liabilities, in particular risks associated with loss of professional registration. The belief that they are more likely to be blamed than supported by their organisation in the event of an incident was cited by staff as a source of additional anxiety when making more complex decisions. This perceived vulnerability can provoke excessively risk-averse decisions. These issues merit further attention to examine the workforce implication of service delivery changes, including how to ensure that staff are appropriately equipped and supported to deal effectively with the demands of their role.

Paramedics identified a degree of progress in relation to the profile of patient safety within their organisations but the apparent desire within trusts to prioritise safety improvement was felt to be constrained by service demands and available resources. Attempts to prioritise patient safety appear to focus on ensuring that formal systems are in place (e.g. reporting and communication). Concerns were expressed over how well these systems function to support improvement, for example how incident reports are responded to and whether lessons learned are communicated to ambulance staff within and between trusts. Consideration could be given to identifying ways of supporting ambulance service trusts to develop the safety culture within their organisation.

Service users attributed the increased demand for ambulance services to difficulties in identifying and accessing alternatives. They were receptive to non-conveyance options but felt that lack of awareness of staff roles and skills may cause concern when patients expect conveyance to A&E.

• Recommendations for research

The workshop attendees identified a range of areas for attention in relation to intervention and research, which are provided in Chapter 6 (see Suggestions for potential interventions and research ). The following recommendations for research are based on the study findings:

• Limited and variable access to services in the wider health and social care system is a significant barrier to reducing inappropriate conveyance to A&E. More research is needed to identify effective ways of improving the delivery of care across service boundaries, particularly for patients with limited options at present (e.g. those with mental health issues, those at the end of life and older patients). Research should address structural and attitudinal barriers and how these might be overcome.
• Ambulance services are increasingly focused on reducing conveyance to A&E and they need to ensure that there is an appropriately skilled workforce to minimise the potential risk. The evidence points to at least two issues: (1) training and skills and (2) the cultural profile of paramedics in the NHS, that is, whether others view their decisions as credible. Research could explore the impact of enhanced skills on patient care and on staff, for example the impact of increased training in urgent rather than emergency care. This would also need to address potential cultural barriers to the effective use of new skills.
• Research to explore the impact of different aspects of safety culture on ambulance service staff and the delivery of patient care (e.g. incident reporting, communication, teamworking, and training) could include comparisons across different staff groups and the identification of areas for improvement, as well as interventions that could potentially be tested.
• The increased breadth of decision-making by ambulance service crews with advanced skills includes more diagnostics; therefore, there is a need to look at the diagnostic process and potential causes of error in this environment.
• There is a need to explore whether there are efficient and safe ways of improving telephone triage decisions to reduce over-triage, particularly in relation to calls requiring an 8-minute response. This could include examining training and staffing levels, a higher level of clinician involvement or other forms of decision support.
• There is a need to explore public awareness of, attitudes towards, beliefs about and expectations of the ambulance service and the wider urgent and emergency care network and the scope for behaviour change interventions, for example communication of information about access to and use of services; empowering the public through equipping them with the skills to directly access the services that best meet their needs; and informing the public about the self-management of chronic conditions.
• A number of performance measures were identified engendering perverse motivations leading to suboptimal resource utilisation. An ongoing NIHR Programme Grant for Applied Research (RP-PG-0609–10195; ‘Pre-hospital Outcomes for Evidence-Based Evaluation’) aims to develop new ways of measuring ambulance service performance. It is important that evaluations of new performance metrics or other innovations (e.g. Make Ready ambulances, potential telehealth technologies or decision-support tools) address their potential impact on patient safety.

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• Cite this Page O’Hara R, Johnson M, Hirst E, et al. A qualitative study of decision-making and safety in ambulance service transitions. Southampton (UK): NIHR Journals Library; 2014 Dec. (Health Services and Delivery Research, No. 2.56.) Chapter 8, Conclusions and recommendations.
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