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How to Write and Publish a Research Paper for a Peer-Reviewed Journal

• Open access
• Published: 30 April 2020
• Volume 36 , pages 909–913, ( 2021 )

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• Clara Busse   ORCID: orcid.org/0000-0002-0178-1000 1 &
• Ella August   ORCID: orcid.org/0000-0001-5151-1036 1 , 2  

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Communicating research findings is an essential step in the research process. Often, peer-reviewed journals are the forum for such communication, yet many researchers are never taught how to write a publishable scientific paper. In this article, we explain the basic structure of a scientific paper and describe the information that should be included in each section. We also identify common pitfalls for each section and recommend strategies to avoid them. Further, we give advice about target journal selection and authorship. In the online resource 1 , we provide an example of a high-quality scientific paper, with annotations identifying the elements we describe in this article.

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Avoid common mistakes on your manuscript.

Introduction

Writing a scientific paper is an important component of the research process, yet researchers often receive little formal training in scientific writing. This is especially true in low-resource settings. In this article, we explain why choosing a target journal is important, give advice about authorship, provide a basic structure for writing each section of a scientific paper, and describe common pitfalls and recommendations for each section. In the online resource 1 , we also include an annotated journal article that identifies the key elements and writing approaches that we detail here. Before you begin your research, make sure you have ethical clearance from all relevant ethical review boards.

Select a Target Journal Early in the Writing Process

We recommend that you select a “target journal” early in the writing process; a “target journal” is the journal to which you plan to submit your paper. Each journal has a set of core readers and you should tailor your writing to this readership. For example, if you plan to submit a manuscript about vaping during pregnancy to a pregnancy-focused journal, you will need to explain what vaping is because readers of this journal may not have a background in this topic. However, if you were to submit that same article to a tobacco journal, you would not need to provide as much background information about vaping.

Information about a journal’s core readership can be found on its website, usually in a section called “About this journal” or something similar. For example, the Journal of Cancer Education presents such information on the “Aims and Scope” page of its website, which can be found here: https://www.springer.com/journal/13187/aims-and-scope .

Peer reviewer guidelines from your target journal are an additional resource that can help you tailor your writing to the journal and provide additional advice about crafting an effective article [ 1 ]. These are not always available, but it is worth a quick web search to find out.

Identify Author Roles Early in the Process

Early in the writing process, identify authors, determine the order of authors, and discuss the responsibilities of each author. Standard author responsibilities have been identified by The International Committee of Medical Journal Editors (ICMJE) [ 2 ]. To set clear expectations about each team member’s responsibilities and prevent errors in communication, we also suggest outlining more detailed roles, such as who will draft each section of the manuscript, write the abstract, submit the paper electronically, serve as corresponding author, and write the cover letter. It is best to formalize this agreement in writing after discussing it, circulating the document to the author team for approval. We suggest creating a title page on which all authors are listed in the agreed-upon order. It may be necessary to adjust authorship roles and order during the development of the paper. If a new author order is agreed upon, be sure to update the title page in the manuscript draft.

In the case where multiple papers will result from a single study, authors should discuss who will author each paper. Additionally, authors should agree on a deadline for each paper and the lead author should take responsibility for producing an initial draft by this deadline.

Structure of the Introduction Section

The introduction section should be approximately three to five paragraphs in length. Look at examples from your target journal to decide the appropriate length. This section should include the elements shown in Fig.  1 . Begin with a general context, narrowing to the specific focus of the paper. Include five main elements: why your research is important, what is already known about the topic, the “gap” or what is not yet known about the topic, why it is important to learn the new information that your research adds, and the specific research aim(s) that your paper addresses. Your research aim should address the gap you identified. Be sure to add enough background information to enable readers to understand your study. Table 1 provides common introduction section pitfalls and recommendations for addressing them.

The main elements of the introduction section of an original research article. Often, the elements overlap

Methods Section

The purpose of the methods section is twofold: to explain how the study was done in enough detail to enable its replication and to provide enough contextual detail to enable readers to understand and interpret the results. In general, the essential elements of a methods section are the following: a description of the setting and participants, the study design and timing, the recruitment and sampling, the data collection process, the dataset, the dependent and independent variables, the covariates, the analytic approach for each research objective, and the ethical approval. The hallmark of an exemplary methods section is the justification of why each method was used. Table 2 provides common methods section pitfalls and recommendations for addressing them.

Results Section

The focus of the results section should be associations, or lack thereof, rather than statistical tests. Two considerations should guide your writing here. First, the results should present answers to each part of the research aim. Second, return to the methods section to ensure that the analysis and variables for each result have been explained.

Begin the results section by describing the number of participants in the final sample and details such as the number who were approached to participate, the proportion who were eligible and who enrolled, and the number of participants who dropped out. The next part of the results should describe the participant characteristics. After that, you may organize your results by the aim or by putting the most exciting results first. Do not forget to report your non-significant associations. These are still findings.

Tables and figures capture the reader’s attention and efficiently communicate your main findings [ 3 ]. Each table and figure should have a clear message and should complement, rather than repeat, the text. Tables and figures should communicate all salient details necessary for a reader to understand the findings without consulting the text. Include information on comparisons and tests, as well as information about the sample and timing of the study in the title, legend, or in a footnote. Note that figures are often more visually interesting than tables, so if it is feasible to make a figure, make a figure. To avoid confusing the reader, either avoid abbreviations in tables and figures, or define them in a footnote. Note that there should not be citations in the results section and you should not interpret results here. Table 3 provides common results section pitfalls and recommendations for addressing them.

Discussion Section

Opposite the introduction section, the discussion should take the form of a right-side-up triangle beginning with interpretation of your results and moving to general implications (Fig.  2 ). This section typically begins with a restatement of the main findings, which can usually be accomplished with a few carefully-crafted sentences.

Major elements of the discussion section of an original research article. Often, the elements overlap

Next, interpret the meaning or explain the significance of your results, lifting the reader’s gaze from the study’s specific findings to more general applications. Then, compare these study findings with other research. Are these findings in agreement or disagreement with those from other studies? Does this study impart additional nuance to well-accepted theories? Situate your findings within the broader context of scientific literature, then explain the pathways or mechanisms that might give rise to, or explain, the results.

Journals vary in their approach to strengths and limitations sections: some are embedded paragraphs within the discussion section, while some mandate separate section headings. Keep in mind that every study has strengths and limitations. Candidly reporting yours helps readers to correctly interpret your research findings.

The next element of the discussion is a summary of the potential impacts and applications of the research. Should these results be used to optimally design an intervention? Does the work have implications for clinical protocols or public policy? These considerations will help the reader to further grasp the possible impacts of the presented work.

Finally, the discussion should conclude with specific suggestions for future work. Here, you have an opportunity to illuminate specific gaps in the literature that compel further study. Avoid the phrase “future research is necessary” because the recommendation is too general to be helpful to readers. Instead, provide substantive and specific recommendations for future studies. Table 4 provides common discussion section pitfalls and recommendations for addressing them.

Follow the Journal’s Author Guidelines

After you select a target journal, identify the journal’s author guidelines to guide the formatting of your manuscript and references. Author guidelines will often (but not always) include instructions for titles, cover letters, and other components of a manuscript submission. Read the guidelines carefully. If you do not follow the guidelines, your article will be sent back to you.

Finally, do not submit your paper to more than one journal at a time. Even if this is not explicitly stated in the author guidelines of your target journal, it is considered inappropriate and unprofessional.

Your title should invite readers to continue reading beyond the first page [ 4 , 5 ]. It should be informative and interesting. Consider describing the independent and dependent variables, the population and setting, the study design, the timing, and even the main result in your title. Because the focus of the paper can change as you write and revise, we recommend you wait until you have finished writing your paper before composing the title.

Be sure that the title is useful for potential readers searching for your topic. The keywords you select should complement those in your title to maximize the likelihood that a researcher will find your paper through a database search. Avoid using abbreviations in your title unless they are very well known, such as SNP, because it is more likely that someone will use a complete word rather than an abbreviation as a search term to help readers find your paper.

After you have written a complete draft, use the checklist (Fig. 3 ) below to guide your revisions and editing. Additional resources are available on writing the abstract and citing references [ 5 ]. When you feel that your work is ready, ask a trusted colleague or two to read the work and provide informal feedback. The box below provides a checklist that summarizes the key points offered in this article.

Checklist for manuscript quality

Data Availability

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Lang TA (2017) Writing a better research article. J Public Health Emerg. https://doi.org/10.21037/jphe.2017.11.06

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Acknowledgments

Ella August is grateful to the Sustainable Sciences Institute for mentoring her in training researchers on writing and publishing their research.

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Busse, C., August, E. How to Write and Publish a Research Paper for a Peer-Reviewed Journal. J Canc Educ 36 , 909–913 (2021). https://doi.org/10.1007/s13187-020-01751-z

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Published on 1.5.2024 in Vol 26 (2024)

Integrating Text and Image Analysis: Exploring GPT-4V’s Capabilities in Advanced Radiological Applications Across Subspecialties

Authors of this article:

Research Letter

• Felix Busch 1 , MD   ; 
• Tianyu Han 2 , PhD   ; 
• Marcus R Makowski 3 , MD, PhD   ; 
• Daniel Truhn 2 , MSc, MD   ; 
• Keno K Bressem 4 * , MD   ; 
• Lisa Adams 3 * , MD  

1 Department of Neuroradiology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany

2 Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany

3 Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University Munich, Munich, Germany

4 Institute for Radiology and Nuclear Medicine, German Heart Center Munich, Technical University of Munich, Munich, Germany

*these authors contributed equally

Corresponding Author:

Felix Busch, MD

Department of Neuroradiology

Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin

Charitépl. 1

Berlin, 10117

Phone: 49 3045050

Email: [email protected]

This study demonstrates that GPT-4V outperforms GPT-4 across radiology subspecialties in analyzing 207 cases with 1312 images from the Radiological Society of North America Case Collection.

Introduction

The launch of GPT-4 has generated significant interest in the scientific and medical communities, demonstrating its potential in medicine with notable achievements such as an 83.76% zero-shot accuracy on the United States Medical Licensing Examination (USMLE) [ 1 ]. In radiology, GPT has spanned text-based tasks, including board exam question scoring, data mining, and report structuring [ 2 , 3 ]. The recent release of GPT-4’s visual capabilities (GPT-4V) enables the combined analysis of text and visual data [ 4 ]. Our study focuses on evaluating the diagnostic capabilities of GPT-4V by comparing it to GPT-4 in advanced radiological tasks, benchmarking the potential of this multimodal large language model in the medical imaging field.

We sourced 207 cases with 1312 images from the Radiological Society of North America (RSNA) Case Collection (accessible for RSNA members on the RSNA Case Collection website [ 5 ]), aiming to cover at least 10 cases for each of the 22 presented subspecialties. The cases within each subspeciality were chosen to present different pathologies. Each case had varying numbers of images and were usually labeled for more than 1 subspecialty, so that the total number of cases per subspeciality varied between 1 (for “Physics and Basic Science,” no more than 1 case was available) and 43 (for “Gastrointestinal,” 10 cases in this category were chosen, with 33 additional cases from other subspecialties that were also labeled for “Gastrointestinal”).

GPT-4 and GPT-4V were accessed between November 6, 2023, and November 17, 2024. We utilized an application programming interface (API) account, which allowed us to use the models programmatically and ensure a consistent environment for each test. This access level was crucial, as it provided stable and repeatable interactions with the models, unlike what might be experienced with fluctuating conditions of regular account usage. The ground truth was established based on the final diagnoses stated in the RSNA case entries. We prompted each model 3 times via the API for the following two tasks: first, the models were asked to identify the diagnosis and 2 differentials (providing the patient history only for GPT-4 or patient history with images for GPT-4V); second, the models were asked to answer corresponding multiple-choice questions from the RSNA Case Collection. The GPT-4V assessment used a “chain-of-thought” prompt that guided the model through diagnostic reasoning ( Figure 1 ), in contrast to the text-only assessment of GPT-4. For both tasks, a case was considered correctly diagnosed if the same correct result appeared for at least 2 of 3 prompts. Cases with no repeated correct diagnoses and cases with only false diagnoses across the 3 prompts were marked as incorrectly diagnosed. Mean accuracies and bootstrapped 95% CIs were calculated, and statistical significance was determined by using the McNemar test ( P <.001).

GPT-4 accurately identified the primary diagnosis in 18% (95% CI 12%-25%) of cases (first task). When including differential diagnoses, this accuracy increased to 28% (95% CI 22%-33%). In contrast, GPT-4V achieved a 27% (95% CI 21%-34%) accuracy rate for primary diagnosis, which increased to 35% (95% CI 29%-40%) when differential diagnoses were accounted for. After being presented with multiple-choice questions, including information about clinical history and presentation (second task), GPT-4 achieved an accuracy of 47% (95% CI 42%-56%). Again, GPT-4V demonstrated a higher accuracy of 64% (95% CI 59%-72%). The observed difference in performance was statistically significant ( P <.001). Across 15 subspecialties, GPT-4V outperformed GPT-4, with the sole exception being in “Cardiac Imaging.” Figure 2 summarizes the accuracies across all subspecialties.

Our study shows that GPT-4V has improved performance over GPT-4 in solving complex radiological problems, indicating its potential to detect pathological features in medical images and thus its radiological domain knowledge. The RSNA Case Collection, which is aimed at expert-level professional radiologists, highlights the promise of GPT-4V in specialized medical contexts.

However, the use of GPT-4V warrants a cautious approach. At this time, it should be considered, at best, as a supplemental tool to augment—not replace—the comprehensive analyses performed by trained medical professionals.

Extending the initial research by Yang et al [ 6 ], our study explores the medical image analysis capabilities of GPT-4V in more complex scenarios and with a wider range of cases. The ongoing development of multimodal models, such as Med-Flamingo, for medical applications signals a growing interest in this area [ 7 ].

One challenge is the scarcity of specialized medical data sets. As our study used RSNA member–exclusive cases, it was unlikely that these cases were in GPT-4V’s training data; thus, the risk of data contamination was minimized. However, the corresponding images for each case were indented to highlight specific pathologies, and this does not fully replicate clinical practice, where one would have to analyze each separate image to identify potential pathologies—a task that specialized deep learning models would be better suited to perform.

Future efforts should focus on detailed performance comparisons between generalist models (like GPT-4V) and emerging, radiological domain–specialized, artificial intelligence diagnostic models to clarify the clinical relevance and applicability of generalist models in clinical practice.

Our results encourage conducting further performance evaluations of multimodal models in different radiologic subdisciplines, as well as using larger data sets, to gain a more holistic understanding of their role in radiology.

Data Availability

The cases analyzed in this study are available from the Radiological Society of North America (RSNA) Case Collection. This repository can be accessed by RSNA members on the RSNA Case Collection website [ 5 ], where each case is presented with detailed clinical information, imaging data, questions, multiple-choice answers, and diagnostic conclusions. The data set was utilized under the terms and conditions provided by the RSNA, which permits the use of these cases for educational and research purposes. No additional unpublished data from these cases were utilized in this study. Researchers and readers are encouraged to directly access the RSNA Case Collection for further information.

Conflicts of Interest

None declared.

• Nori H, King N, McKinney SM, Carignan D, Horvitz E. Capabilities of GPT-4 on medical challenge problems. arXiv. Preprint posted online on Apr 12, 2023. [ FREE Full text ] [ CrossRef ]
• Bhayana R, Krishna S, Bleakney RR. Performance of ChatGPT on a radiology board-style examination: insights into current strengths and limitations. Radiology. Jun 2023;307(5):e230582. [ CrossRef ] [ Medline ]
• Adams LC, Truhn D, Busch F, Kader A, Niehues SM, Makowski MR, et al. Leveraging GPT-4 for post hoc transformation of free-text radiology reports into structured reporting: a multilingual feasibility study. Radiology. May 2023;307(4):e230725. [ CrossRef ] [ Medline ]
• GPT-4V(ision) system card. OpenAI. Sep 25, 2023. URL: https://openai.com/research/gpt-4v-system-card [accessed 2023-10-14]
• RSNA Case Collection. Radiological Society of North America. URL: https://cases.rsna.org/ [accessed 2024-04-24]
• Yang Z, Li L, Lin K, Wang J, Lin CC, Liu Z, et al. The dawn of LMMs: preliminary explorations with GPT-4V(ision). arXiv. Preprint posted online on Oct 11, 2023. [ FREE Full text ] [ CrossRef ]
• Moor M, Huang Q, Wu S, Yasunaga M, Zakka C, Dalmia Y, et al. Med-Flamingo: a multimodal medical few-shot learner. arXiv. Preprint posted online on Jul 27, 2023. [ FREE Full text ] [ CrossRef ]

Abbreviations

Edited by G Eysenbach; submitted 28.11.23; peer-reviewed by L Zhu, S Kommireddy, H Younes; comments to author 06.02.24; revised version received 10.02.24; accepted 20.03.24; published 01.05.24.

©Felix Busch, Tianyu Han, Marcus R Makowski, Daniel Truhn, Keno K Bressem, Lisa Adams. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 01.05.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

How to find the right journal for your research (using actual data)

Joanna Wilkinson

Want to help your research flourish? We share tips for using publisher-neutral data and statistics to find the right journal for your research paper.

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With so many points to consider, it’s good practice to have a journal in mind before you start writing. We published an earlier post to help you with this: Find top journals in a research field, step-by-step guide . Check it out to discover where the top researchers in your field are publishing.

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• 29 April 2024

How reliable is this research? Tool flags papers discussed on PubPeer

• Dalmeet Singh Chawla

You can also search for this author in PubMed   Google Scholar

RedacTek’s tool alerts users to PubPeer discussions, and indicates when a study, or the papers that it cites, has been retracted. Credit: deepblue4you/Getty

A free online tool released earlier this month alerts researchers when a paper cites studies that are mentioned on the website PubPeer , a forum scientists often use to raise integrity concerns surrounding published papers.

Studies are usually flagged on PubPeer when readers have suspicions, for example about image manipulation , plagiarism , data fabrication or artificial intelligence (AI)-generated text . PubPeer already offers its own browser plug-in that alerts users when a study that they are reading has been posted on the site. The new tool, a plug-in released on 13 April by RedacTek , based in Oakland, California, goes further — it searches through reference lists for papers that have been flagged. The software pulls information from many sources, including PubPeer’s database; data from the digital-infrastructure organization Crossref, which assigns digital object identifiers to articles; and OpenAlex , a free index of hundreds of millions of scientific documents.

It’s important to track mentions of referenced articles on PubPeer, says Jodi Schneider, an information scientist at the University of Illinois Urbana-Champaign, who has tried out the RedacTek plug-in. “Not every single reference that’s in the bibliography matters, but some of them do,” she adds. “When you see a large number of problems in somebody’s bibliography, that just calls everything into question.”

The aim of the tool is to flag potential problems with studies to researchers early on, to reduce the circulation of poor-quality science, says RedacTek founder Rick Meyler, based in Emeryville, California. Future versions might also use AI to automatically clarify whether the PubPeer comments on a paper are positive or negative, he adds.

Third-generation retractions

As well as flagging PubPeer discussions, the plug-in indicates when a study, or the papers that it cites, has been retracted. There are existing tools that alert academics about retracted citations ; some can do this during the writing process, so that researchers are aware of the publication status of studies when constructing bibliographies. But with the new tool, users can opt in to receive notifications about further ‘generations’ of retractions — alerts cover not only the study that they are reading, but also the papers it cites, articles cited by those references and even papers cited by the secondary references.

The software also calculates a ‘retraction association value’ for studies, a metric that measures the extent to which the paper is associated with science that has been withdrawn from the literature. As well as informing individual researchers, the plug-in could help scholarly publishers to keep tabs on their own journals, Meyler says, because it allows users to filter by publication.

In its ‘paper scorecard’, the tool also flags any papers in the three generations of referenced studies in which more than 25% of papers in the bibliography are self-citations — references by authors to their previous works.

Future versions could highlight whether papers cited retracted studies before or after the retraction was issued, notes Meyler, or whether mentions of such studies acknowledge the retraction. That would be useful, says Schneider, who co-authored a 2020 analysis that found that as little as 4% of citations to retracted studies note that the referenced paper has been retracted 1 .

Meyler says that RedacTek is currently in talks with scholarly-services firm Cabell’s International in Beaumont, Texas, which maintains pay-to-view lists of suspected predatory journals , which publish articles without proper quality checks for issues such as plagiarism but still collect authors’ fees. The plan is to use these lists to improve the tool so that it can also automatically flag any cited papers that are published in such journals.

doi: https://doi.org/10.1038/d41586-024-01247-6

Schneider, J., Ye, D., Hill, A. M. & Whitehorn, A. S. Scientometrics 125 , 2877–2913 (2020).

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Research Award Recognizes the Importance of Surfactant Stability

• ACI Distinguished Paper Award Presented at AOCS Annual Meeting

Research that could be used to inform surfactant formulation has been recognized as the best paper published in 2023 in the Journal of Surfactants and Detergents . 

The authors were recognized during the  2024 American Oil Chemists’ Society (AOCS) Annual Meeting , held April 28-May 1 in Montréal, Québec, Canada.

The Distinguished Paper Award is an annual honor presented by the American Cleaning Institute ® (ACI). 

The authors of the research include Dan Lundberg, AstraZeneca R&D, Maria Stjerndahl, Sahlgrenska University Hospital and Krister Holmberg, Department of Chemistry and Chemical Engineering, Chalmers University of Technology in Gothenburg, Sweden.

Their article reviews the alkaline hydrolysis of cationic, anionic and nonionic ester-based surfactants and surfactant mixtures. Cationic surfactants readily undergo alkaline hydrolysis. Anionic ester-based surfactants are highly stable and can be used in alkaline formulations that when diluted to a level below the critical micelle concentration (e.g., in a wastewater treatment plant), the hydrolysis rate will quicken and facilitate biodegradation. Nonionic ester surfactants can behave similarly to anionic ester surfactants in the formation of micelles that inhibit hydrolysis.

Household cleaning products are typically formulated using mixtures of different surfactants to achieve hydrolysis patterns that are advantageous for different cleaning applications. This review article summarizes the current state of the science on surfactant features that are important to consider during their synthesis.

“We are thrilled to be presented with this award,” said the paper’s authors. “We believe that the issue of stability of ester-based surfactants is important both from a user’s perspective and from an environmental point of view.”

The paper, “ Ester-based surfactants: Are they stable enough? ,” was published in the Journal of Surfactants and Detergents, May 2023, Volume 26, Issue 3, pp 223-451.

The American Cleaning Institute ® (ACI – www.cleaninginstitute.org) is the Home of the U.S. Cleaning Products Industry ® and represents the $60 billion U.S. cleaning product supply chain. ACI members include the manufacturers and formulators of soaps, detergents, and general cleaning products used in household, commercial, industrial and institutional settings; companies that supply ingredients and finished packaging for these products; and chemical distributors. ACI serves the growth and innovation of the U.S. cleaning products industry by advancing the health and quality of life of people and protecting our planet. ACI achieves this through a continuous commitment to sound science and being a credible voice for the cleaning products industry.