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Systematic reviews & evidence synthesis methods.

• Schedule a Consultation / Meet our Team
• What is Evidence Synthesis?
• Types of Evidence Synthesis
• Evidence Synthesis Across Disciplines
• Finding and Appraising Existing Systematic Reviews
• 1. Develop a Protocol
• 2. Draft your Research Question
• 3. Select Databases
• 4. Select Grey Literature Sources
• 5. Write a Search Strategy
• 6. Register a Protocol
• 7. Translate Search Strategies
• 8. Citation Management
• 9. Article Screening
• 10. Risk of Bias Assessment
• 11. Data Extraction
• 12. Synthesize, Map, or Describe the Results
• Open Access Evidence Synthesis Resources

What are Evidence Syntheses?

According to the Royal Society, 'evidence synthesis' refers to the process of bringing together information from a range of sources and disciplines to inform debates and decisions on specific issues. They generally include a methodical and comprehensive literature synthesis focused on a well-formulated research question. Their aim is to identify and synthesize all of the scholarly research on a particular topic, including both published and unpublished studies. Evidence syntheses are conducted in an unbiased, reproducible way to provide evidence for practice and policy-making, as well as to identify gaps in the research. Evidence syntheses may also include a meta-analysis, a more quantitative process of synthesizing and visualizing data retrieved from various studies.

Evidence syntheses are much more time-intensive than traditional literature reviews and require a multi-person research team. See this PredicTER tool to get a sense of a systematic review timeline (one type of evidence synthesis). Before embarking on an evidence synthesis, it's important to clearly identify your reasons for conducting one. For a list of types of evidence synthesis projects, see the Types of Evidence Synthesis tab.

How Does a Traditional Literature Review Differ From an Evidence Synthesis?

One commonly used form of evidence synthesis is a systematic review. This table compares a traditional literature review with a systematic review.

Video: Reproducibility and transparent methods (Video 3:25)

Reporting Standards

There are some reporting standards for evidence syntheses. These can serve as guidelines for protocol and manuscript preparation and journals may require that these standards are followed for the review type that is being employed (e.g. systematic review, scoping review, etc).​

• PRISMA checklist Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) is an evidence-based minimum set of items for reporting in systematic reviews and meta-analyses.
• PRISMA-P Standards An updated version of the original PRISMA standards for protocol development.
• PRISMA - ScR Reporting guidelines for scoping reviews and evidence maps
• PRISMA-IPD Standards Extension of the original PRISMA standards for systematic reviews and meta-analyses of individual participant data.
• EQUATOR Network The EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network is an international initiative that seeks to improve the reliability and value of published health research literature by promoting transparent and accurate reporting and wider use of robust reporting guidelines. They provide a list of various standards for reporting in systematic reviews.

Video: Guidelines and reporting standards

PRISMA Flow Diagram

The PRISMA flow diagram depicts the flow of information through the different phases of an evidence synthesis. It maps the search (number of records identified), screening (number of records included and excluded), and selection (reasons for exclusion). Many evidence syntheses include a PRISMA flow diagram in the published manuscript.

See below for resources to help you generate your own PRISMA flow diagram.

• PRISMA Flow Diagram Tool
• PRISMA Flow Diagram Word Template
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A Guide to Evidence Synthesis: Types of Evidence Synthesis

• Meet Our Team
• Our Published Reviews and Protocols
• What is Evidence Synthesis?

Types of Evidence Synthesis

• Evidence Synthesis Across Disciplines
• Finding and Appraising Existing Systematic Reviews
• 0. Develop a Protocol
• 1. Draft your Research Question
• 2. Select Databases
• 3. Select Grey Literature Sources
• 4. Write a Search Strategy
• 5. Register a Protocol
• 6. Translate Search Strategies
• 7. Citation Management
• 8. Article Screening
• 9. Risk of Bias Assessment
• 10. Data Extraction
• 11. Synthesize, Map, or Describe the Results
• Evidence Synthesis Institute for Librarians
• Open Access Evidence Synthesis Resources

Video: Exploring different review methodologies (3:25 minutes)

Evidence synthesis refers to  any method of identifying, selecting, and combining results from multiple studies . For help selecting a methodology, try our review methodology decision tree. Types of evidence synthesis include: 

​​ Systematic Review

• Systematically and transparently collect and  categorize  existing evidence on a broad question of scientific,  policy or management importance.
• Compares, evaluates, and synthesizes evidence in a search for the effect of an intervention. 
• Time-intensive and often take months to a year or more to complete. 
• The most commonly referred to type of evidence synthesis. Sometimes confused as a blanket term for other types of reviews.

​​ Literature (Narrative) Review

• A broad term referring to reviews with a wide scope and non-standardized methodology. 
• Search strategies, comprehensiveness, and time range covered will vary and do not follow an established protocol.

​ Scoping Review or Evidence Map

• Seeks to identify research gaps and opportunities for evidence synthesis rather than searching for the effect of an intervention. 
• May critically evaluate existing evidence, but does not attempt to synthesize the results in the way a systematic review would. (see  EE Journal  and  CIFOR )
• May take longer than a systematic review.
• See  Arksey and O'Malley (2005)  for methodological guidance.

​ Rapid Review

• Applies Systematic Review methodology within a time-constrained setting.
• Employs methodological "shortcuts" (limiting search terms for example) at the risk of introducing bias.
• Useful for addressing issues needing quick decisions, such as developing policy recommendations.
• See  Evidence Summaries: The Evolution of a Rapid Review Approach

Umbrella Review

• Reviews other systematic reviews on a topic. 
• Often defines a broader question than is typical of a traditional systematic review.
• Most useful when there are competing interventions to consider.

Meta-analysis

• Statistical technique for combining the findings from disparate quantitative studies.
• Uses statistical methods to objectively evaluate, synthesize, and summarize results.
• May be conducted independently or as part of a systematic review.
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Cochrane Training

Chapter 12: synthesizing and presenting findings using other methods.

Joanne E McKenzie, Sue E Brennan

Key Points:

• Meta-analysis of effect estimates has many advantages, but other synthesis methods may need to be considered in the circumstance where there is incompletely reported data in the primary studies.
• Alternative synthesis methods differ in the completeness of the data they require, the hypotheses they address, and the conclusions and recommendations that can be drawn from their findings.
• These methods provide more limited information for healthcare decision making than meta-analysis, but may be superior to a narrative description where some results are privileged above others without appropriate justification.
• Tabulation and visual display of the results should always be presented alongside any synthesis, and are especially important for transparent reporting in reviews without meta-analysis.
• Alternative synthesis and visual display methods should be planned and specified in the protocol. When writing the review, details of the synthesis methods should be described.
• Synthesis methods that involve vote counting based on statistical significance have serious limitations and are unacceptable.

Cite this chapter as: McKenzie JE, Brennan SE. Chapter 12: Synthesizing and presenting findings using other methods. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.4 (updated August 2023). Cochrane, 2023. Available from www.training.cochrane.org/handbook .

12.1 Why a meta-analysis of effect estimates may not be possible

Meta-analysis of effect estimates has many potential advantages (see Chapter 10 and Chapter 11 ). However, there are circumstances where it may not be possible to undertake a meta-analysis and other statistical synthesis methods may be considered (McKenzie and Brennan 2014).

Some common reasons why it may not be possible to undertake a meta-analysis are outlined in Table 12.1.a . Legitimate reasons include limited evidence; incompletely reported outcome/effect estimates, or different effect measures used across studies; and bias in the evidence. Other commonly cited reasons for not using meta-analysis are because of too much clinical or methodological diversity, or statistical heterogeneity (Achana et al 2014). However, meta-analysis methods should be considered in these circumstances, as they may provide important insights if undertaken and interpreted appropriately.

Table 12.1.a Scenarios that may preclude meta-analysis, with possible solutions

12.2 Statistical synthesis when meta-analysis of effect estimates is not possible

A range of statistical synthesis methods are available, and these may be divided into three categories based on their preferability ( Table 12.2.a ). Preferable methods are the meta-analysis methods outlined in Chapter 10 and Chapter 11 , and are not discussed in detail here. This chapter focuses on methods that might be considered when a meta-analysis of effect estimates is not possible due to incompletely reported data in the primary studies. These methods divide into those that are ‘acceptable’ and ‘unacceptable’. The ‘acceptable’ methods differ in the data they require, the hypotheses they address, limitations around their use, and the conclusions and recommendations that can be drawn (see Section 12.2.1 ). The ‘unacceptable’ methods in common use are described (see Section 12.2.2 ), along with the reasons for why they are problematic.

Compared with meta-analysis methods, the ‘acceptable’ synthesis methods provide more limited information for healthcare decision making. However, these ‘acceptable’ methods may be superior to a narrative that describes results study by study, which comes with the risk that some studies or findings are privileged above others without appropriate justification. Further, in reviews with little or no synthesis, readers are left to make sense of the research themselves, which may result in the use of seemingly simple yet problematic synthesis methods such as vote counting based on statistical significance (see Section 12.2.2.1 ).

All methods first involve calculation of a ‘standardized metric’, followed by application of a synthesis method. In applying any of the following synthesis methods, it is important that only one outcome per study (or other independent unit, for example one comparison from a trial with multiple intervention groups) contributes to the synthesis. Chapter 9 outlines approaches for selecting an outcome when multiple have been measured. Similar to meta-analysis, sensitivity analyses can be undertaken to examine if the findings of the synthesis are robust to potentially influential decisions (see Chapter 10, Section 10.14 and Section 12.4 for examples).

Authors should report the specific methods used in lieu of meta-analysis (including approaches used for presentation and visual display), rather than stating that they have conducted a ‘narrative synthesis’ or ‘narrative summary’ without elaboration. The limitations of the chosen methods must be described, and conclusions worded with appropriate caution. The aim of reporting this detail is to make the synthesis process more transparent and reproducible, and help ensure use of appropriate methods and interpretation.

Table 12.2.a Summary of preferable and acceptable synthesis methods

12.2.1 Acceptable synthesis methods

12.2.1.1 summarizing effect estimates.

Description of method Summarizing effect estimates might be considered in the circumstance where estimates of intervention effect are available (or can be calculated), but the variances of the effects are not reported or are incorrect (and cannot be calculated from other statistics, or reasonably imputed) (Grimshaw et al 2003). Incorrect calculation of variances arises more commonly in non-standard study designs that involve clustering or matching ( Chapter 23 ). While missing variances may limit the possibility of meta-analysis, the (standardized) effects can be summarized using descriptive statistics such as the median, interquartile range, and the range. Calculating these statistics addresses the question ‘What is the range and distribution of observed effects?’

Reporting of methods and results The statistics that will be used to summarize the effects (e.g. median, interquartile range) should be reported. Box-and-whisker or bubble plots will complement reporting of the summary statistics by providing a visual display of the distribution of observed effects (Section 12.3.3 ). Tabulation of the available effect estimates will provide transparency for readers by linking the effects to the studies (Section 12.3.1 ). Limitations of the method should be acknowledged ( Table 12.2.a ).

12.2.1.2 Combining P values

Description of method Combining P values can be considered in the circumstance where there is no, or minimal, information reported beyond P values and the direction of effect; the types of outcomes and statistical tests differ across the studies; or results from non-parametric tests are reported (Borenstein et al 2009). Combining P values addresses the question ‘Is there evidence that there is an effect in at least one study?’ There are several methods available (Loughin 2004), with the method proposed by Fisher outlined here (Becker 1994).

Fisher’s method combines the P values from statistical tests across k studies using the formula:

One-sided P values are used, since these contain information about the direction of effect. However, these P values must reflect the same directional hypothesis (e.g. all testing if intervention A is more effective than intervention B). This is analogous to standardizing the direction of effects before undertaking a meta-analysis. Two-sided P values, which do not contain information about the direction, must first be converted to one-sided P values. If the effect is consistent with the directional hypothesis (e.g. intervention A is beneficial compared with B), then the one-sided P value is calculated as

In studies that do not report an exact P value but report a conventional level of significance (e.g. P<0.05), a conservative option is to use the threshold (e.g. 0.05). The P values must have been computed from statistical tests that appropriately account for the features of the design, such as clustering or matching, otherwise they will likely be incorrect.

Reporting of methods and results There are several methods for combining P values (Loughin 2004), so the chosen method should be reported, along with details of sensitivity analyses that examine if the results are sensitive to the choice of method. The results from the test should be reported alongside any available effect estimates (either individual results or meta-analysis results of a subset of studies) using text, tabulation and appropriate visual displays (Section 12.3 ). The albatross plot is likely to complement the analysis (Section 12.3.4 ). Limitations of the method should be acknowledged ( Table 12.2.a ).

12.2.1.3 Vote counting based on the direction of effect

Description of method Vote counting based on the direction of effect might be considered in the circumstance where the direction of effect is reported (with no further information), or there is no consistent effect measure or data reported across studies. The essence of vote counting is to compare the number of effects showing benefit to the number of effects showing harm for a particular outcome. However, there is wide variation in the implementation of the method due to differences in how ‘benefit’ and ‘harm’ are defined. Rules based on subjective decisions or statistical significance are problematic and should be avoided (see Section 12.2.2 ).

To undertake vote counting properly, each effect estimate is first categorized as showing benefit or harm based on the observed direction of effect alone, thereby creating a standardized binary metric. A count of the number of effects showing benefit is then compared with the number showing harm. Neither statistical significance nor the size of the effect are considered in the categorization. A sign test can be used to answer the question ‘is there any evidence of an effect?’ If there is no effect, the study effects will be distributed evenly around the null hypothesis of no difference. This is equivalent to testing if the true proportion of effects favouring the intervention (or comparator) is equal to 0.5 (Bushman and Wang 2009) (see Section 12.4.2.3 for guidance on implementing the sign test). An estimate of the proportion of effects favouring the intervention can be calculated ( p = u / n , where u = number of effects favouring the intervention, and n = number of studies) along with a confidence interval (e.g. using the Wilson or Jeffreys interval methods (Brown et al 2001)). Unless there are many studies contributing effects to the analysis, there will be large uncertainty in this estimated proportion.

Reporting of methods and results The vote counting method should be reported in the ‘Data synthesis’ section of the review. Failure to recognize vote counting as a synthesis method has led to it being applied informally (and perhaps unintentionally) to summarize results (e.g. through the use of wording such as ‘3 of 10 studies showed improvement in the outcome with intervention compared to control’; ‘most studies found’; ‘the majority of studies’; ‘few studies’ etc). In such instances, the method is rarely reported, and it may not be possible to determine whether an unacceptable (invalid) rule has been used to define benefit and harm (Section 12.2.2 ). The results from vote counting should be reported alongside any available effect estimates (either individual results or meta-analysis results of a subset of studies) using text, tabulation and appropriate visual displays (Section 12.3 ). The number of studies contributing to a synthesis based on vote counting may be larger than a meta-analysis, because only minimal statistical information (i.e. direction of effect) is required from each study to vote count. Vote counting results are used to derive the harvest and effect direction plots, although often using unacceptable methods of vote counting (see Section 12.3.5 ). Limitations of the method should be acknowledged ( Table 12.2.a ).

12.2.2 Unacceptable synthesis methods

12.2.2.1 vote counting based on statistical significance.

Conventional forms of vote counting use rules based on statistical significance and direction to categorize effects. For example, effects may be categorized into three groups: those that favour the intervention and are statistically significant (based on some predefined P value), those that favour the comparator and are statistically significant, and those that are statistically non-significant (Hedges and Vevea 1998). In a simpler formulation, effects may be categorized into two groups: those that favour the intervention and are statistically significant, and all others (Friedman 2001). Regardless of the specific formulation, when based on statistical significance, all have serious limitations and can lead to the wrong conclusion.

The conventional vote counting method fails because underpowered studies that do not rule out clinically important effects are counted as not showing benefit. Suppose, for example, the effect sizes estimated in two studies were identical. However, only one of the studies was adequately powered, and the effect in this study was statistically significant. Only this one effect (of the two identical effects) would be counted as showing ‘benefit’. Paradoxically, Hedges and Vevea showed that as the number of studies increases, the power of conventional vote counting tends to zero, except with large studies and at least moderate intervention effects (Hedges and Vevea 1998). Further, conventional vote counting suffers the same disadvantages as vote counting based on direction of effect, namely, that it does not provide information on the magnitude of effects and does not account for differences in the relative sizes of the studies.

12.2.2.2 Vote counting based on subjective rules

Subjective rules, involving a combination of direction, statistical significance and magnitude of effect, are sometimes used to categorize effects. For example, in a review examining the effectiveness of interventions for teaching quality improvement to clinicians, the authors categorized results as ‘beneficial effects’, ‘no effects’ or ‘detrimental effects’ (Boonyasai et al 2007). Categorization was based on direction of effect and statistical significance (using a predefined P value of 0.05) when available. If statistical significance was not reported, effects greater than 10% were categorized as ‘beneficial’ or ‘detrimental’, depending on their direction. These subjective rules often vary in the elements, cut-offs and algorithms used to categorize effects, and while detailed descriptions of the rules may provide a veneer of legitimacy, such rules have poor performance validity (Ioannidis et al 2008).

A further problem occurs when the rules are not described in sufficient detail for the results to be reproduced (e.g. ter Wee et al 2012, Thornicroft et al 2016). This lack of transparency does not allow determination of whether an acceptable or unacceptable vote counting method has been used (Valentine et al 2010).

12.3 Visual display and presentation of the data

Visual display and presentation of data is especially important for transparent reporting in reviews without meta-analysis, and should be considered irrespective of whether synthesis is undertaken (see Table 12.2.a for a summary of plots associated with each synthesis method). Tables and plots structure information to show patterns in the data and convey detailed information more efficiently than text. This aids interpretation and helps readers assess the veracity of the review findings.

12.3.1 Structured tabulation of results across studies

Ordering studies alphabetically by study ID is the simplest approach to tabulation; however, more information can be conveyed when studies are grouped in subpanels or ordered by a characteristic important for interpreting findings. The grouping of studies in tables should generally follow the structure of the synthesis presented in the text, which should closely reflect the review questions. This grouping should help readers identify the data on which findings are based and verify the review authors’ interpretation.

If the purpose of the table is comparative, grouping studies by any of following characteristics might be informative:

• comparisons considered in the review, or outcome domains (according to the structure of the synthesis);
• study characteristics that may reveal patterns in the data, for example potential effect modifiers including population subgroups, settings or intervention components.

If the purpose of the table is complete and transparent reporting of data, then ordering the studies to increase the prominence of the most relevant and trustworthy evidence should be considered. Possibilities include:

• certainty of the evidence (synthesized result or individual studies if no synthesis);
• risk of bias, study size or study design characteristics; and
• characteristics that determine how directly a study addresses the review question, for example relevance and validity of the outcome measures.

One disadvantage of grouping by study characteristics is that it can be harder to locate specific studies than when tables are ordered by study ID alone, for example when cross-referencing between the text and tables. Ordering by study ID within categories may partly address this.

The value of standardizing intervention and outcome labels is discussed in Chapter 3, Section 3.2.2 and Section 3.2.4 ), while the importance and methods for standardizing effect estimates is described in Chapter 6 . These practices can aid readers’ interpretation of tabulated data, especially when the purpose of a table is comparative.

12.3.2 Forest plots

Forest plots and methods for preparing them are described elsewhere ( Chapter 10, Section 10.2 ). Some mention is warranted here of their importance for displaying study results when meta-analysis is not undertaken (i.e. without the summary diamond). Forest plots can aid interpretation of individual study results and convey overall patterns in the data, especially when studies are ordered by a characteristic important for interpreting results (e.g. dose and effect size, sample size). Similarly, grouping studies in subpanels based on characteristics thought to modify effects, such as population subgroups, variants of an intervention, or risk of bias, may help explore and explain differences across studies (Schriger et al 2010). These approaches to ordering provide important techniques for informally exploring heterogeneity in reviews without meta-analysis, and should be considered in preference to alphabetical ordering by study ID alone (Schriger et al 2010).

12.3.3 Box-and-whisker plots and bubble plots

Box-and-whisker plots (see Figure 12.4.a , Panel A) provide a visual display of the distribution of effect estimates (Section 12.2.1.1 ). The plot conventionally depicts five values. The upper and lower limits (or ‘hinges’) of the box, represent the 75th and 25th percentiles, respectively. The line within the box represents the 50th percentile (median), and the whiskers represent the extreme values (McGill et al 1978). Multiple box plots can be juxtaposed, providing a visual comparison of the distributions of effect estimates (Schriger et al 2006). For example, in a review examining the effects of audit and feedback on professional practice, the format of the feedback (verbal, written, both verbal and written) was hypothesized to be an effect modifier (Ivers et al 2012). Box-and-whisker plots of the risk differences were presented separately by the format of feedback, to allow visual comparison of the impact of format on the distribution of effects. When presenting multiple box-and-whisker plots, the width of the box can be varied to indicate the number of studies contributing to each. The plot’s common usage facilitates rapid and correct interpretation by readers (Schriger et al 2010). The individual studies contributing to the plot are not identified (as in a forest plot), however, and the plot is not appropriate when there are few studies (Schriger et al 2006).

A bubble plot (see Figure 12.4.a , Panel B) can also be used to provide a visual display of the distribution of effects, and is more suited than the box-and-whisker plot when there are few studies (Schriger et al 2006). The plot is a scatter plot that can display multiple dimensions through the location, size and colour of the bubbles. In a review examining the effects of educational outreach visits on professional practice, a bubble plot was used to examine visually whether the distribution of effects was modified by the targeted behaviour (O’Brien et al 2007). Each bubble represented the effect size (y-axis) and whether the study targeted a prescribing or other behaviour (x-axis). The size of the bubbles reflected the number of study participants. However, different formulations of the bubble plot can display other characteristics of the data (e.g. precision, risk-of-bias assessments).

12.3.4 Albatross plot

The albatross plot (see Figure 12.4.a , Panel C) allows approximate examination of the underlying intervention effect sizes where there is minimal reporting of results within studies (Harrison et al 2017). The plot only requires a two-sided P value, sample size and direction of effect (or equivalently, a one-sided P value and a sample size) for each result. The plot is a scatter plot of the study sample sizes against two-sided P values, where the results are separated by the direction of effect. Superimposed on the plot are ‘effect size contours’ (inspiring the plot’s name). These contours are specific to the type of data (e.g. continuous, binary) and statistical methods used to calculate the P values. The contours allow interpretation of the approximate effect sizes of the studies, which would otherwise not be possible due to the limited reporting of the results. Characteristics of studies (e.g. type of study design) can be identified using different colours or symbols, allowing informal comparison of subgroups.

The plot is likely to be more inclusive of the available studies than meta-analysis, because of its minimal data requirements. However, the plot should complement the results from a statistical synthesis, ideally a meta-analysis of available effects.

12.3.5 Harvest and effect direction plots

Harvest plots (see Figure 12.4.a , Panel D) provide a visual extension of vote counting results (Ogilvie et al 2008). In the plot, studies based on the categorization of their effects (e.g. ‘beneficial effects’, ‘no effects’ or ‘detrimental effects’) are grouped together. Each study is represented by a bar positioned according to its categorization. The bars can be ‘visually weighted’ (by height or width) and annotated to highlight study and outcome characteristics (e.g. risk-of-bias domains, proximal or distal outcomes, study design, sample size) (Ogilvie et al 2008, Crowther et al 2011). Annotation can also be used to identify the studies. A series of plots may be combined in a matrix that displays, for example, the vote counting results from different interventions or outcome domains.

The methods papers describing harvest plots have employed vote counting based on statistical significance (Ogilvie et al 2008, Crowther et al 2011). For the reasons outlined in Section 12.2.2.1 , this can be misleading. However, an acceptable approach would be to display the results based on direction of effect.

The effect direction plot is similar in concept to the harvest plot in the sense that both display information on the direction of effects (Thomson and Thomas 2013). In the first version of the effect direction plot, the direction of effects for each outcome within a single study are displayed, while the second version displays the direction of the effects for outcome domains across studies . In this second version, an algorithm is first applied to ‘synthesize’ the directions of effect for all outcomes within a domain (e.g. outcomes ‘sleep disturbed by wheeze’, ‘wheeze limits speech’, ‘wheeze during exercise’ in the outcome domain ‘respiratory’). This algorithm is based on the proportion of effects that are in a consistent direction and statistical significance. Arrows are used to indicate the reported direction of effect (for either outcomes or outcome domains). Features such as statistical significance, study design and sample size are denoted using size and colour. While this version of the plot conveys a large amount of information, it requires further development before its use can be recommended since the algorithm underlying the plot is likely to have poor performance validity.

12.4 Worked example

The example that follows uses four scenarios to illustrate methods for presentation and synthesis when meta-analysis is not possible. The first scenario contrasts a common approach to tabulation with alternative presentations that may enhance the transparency of reporting and interpretation of findings. Subsequent scenarios show the application of the synthesis approaches outlined in preceding sections of the chapter. Box 12.4.a summarizes the review comparisons and outcomes, and decisions taken by the review authors in planning their synthesis. While the example is loosely based on an actual review, the review description, scenarios and data are fabricated for illustration.

Box 12.4.a The review

12.4.1 Scenario 1: structured reporting of effects

We first address a scenario in which review authors have decided that the tools used to measure satisfaction measured concepts that were too dissimilar across studies for synthesis to be appropriate. Setting aside three of the 15 studies that reported on the birth partner’s satisfaction with care, a structured summary of effects is sought of the remaining 12 studies. To keep the example table short, only one outcome is shown per study for each of the measurement periods (antenatal, intrapartum or postpartum).

Table 12.4.a depicts a common yet suboptimal approach to presenting results. Note two features.

• Studies are ordered by study ID, rather than grouped by characteristics that might enhance interpretation (e.g. risk of bias, study size, validity of the measures, certainty of the evidence (GRADE)).
• Data reported are as extracted from each study; effect estimates were not calculated by the review authors and, where reported, were not standardized across studies (although data were available to do both).

Table 12.4.b shows an improved presentation of the same results. In line with best practice, here effect estimates have been calculated by the review authors for all outcomes, and a common metric computed to aid interpretation (in this case an odds ratio; see Chapter 6 for guidance on conversion of statistics to the desired format). Redundant information has been removed (‘statistical test’ and ‘P value’ columns). The studies have been re-ordered, first to group outcomes by period of care (intrapartum outcomes are shown here), and then by risk of bias. This re-ordering serves two purposes. Grouping by period of care aligns with the plan to consider outcomes for each period separately and ensures the table structure matches the order in which results are described in the text. Re-ordering by risk of bias increases the prominence of studies at lowest risk of bias, focusing attention on the results that should most influence conclusions. Had the review authors determined that a synthesis would be informative, then ordering to facilitate comparison across studies would be appropriate; for example, ordering by the type of satisfaction outcome (as pre-defined in the protocol, starting with global measures of satisfaction), or the comparisons made in the studies.

The results may also be presented in a forest plot, as shown in Figure 12.4.b . In both the table and figure, studies are grouped by risk of bias to focus attention on the most trustworthy evidence. The pattern of effects across studies is immediately apparent in Figure 12.4.b and can be described efficiently without having to interpret each estimate (e.g. difference between studies at low and high risk of bias emerge), although these results should be interpreted with caution in the absence of a formal test for subgroup differences (see Chapter 10, Section 10.11 ). Only outcomes measured during the intrapartum period are displayed, although outcomes from other periods could be added, maximizing the information conveyed.

An example description of the results from Scenario 1 is provided in Box 12.4.b . It shows that describing results study by study becomes unwieldy with more than a few studies, highlighting the importance of tables and plots. It also brings into focus the risk of presenting results without any synthesis, since it seems likely that the reader will try to make sense of the results by drawing inferences across studies. Since a synthesis was considered inappropriate, GRADE was applied to individual studies and then used to prioritize the reporting of results, focusing attention on the most relevant and trustworthy evidence. An alternative might be to report results at low risk of bias, an approach analogous to limiting a meta-analysis to studies at low risk of bias. Where possible, these and other approaches to prioritizing (or ordering) results from individual studies in text and tables should be pre-specified at the protocol stage.

Table 12.4.a Scenario 1: table ordered by study ID, data as reported by study authors

* All scales operate in the same direction; higher scores indicate greater satisfaction. CI = confidence interval; MD = mean difference; OR = odds ratio; POR = proportional odds ratio; RD = risk difference; RR = risk ratio.

Table 12.4.b Scenario 1: intrapartum outcome table ordered by risk of bias, standardized effect estimates calculated for all studies

* Outcomes operate in the same direction. A higher score, or an event, indicates greater satisfaction. ** Mean difference calculated for studies reporting continuous outcomes. † For binary outcomes, odds ratios were calculated from the reported summary statistics or were directly extracted from the study. For continuous outcomes, standardized mean differences were calculated and converted to odds ratios (see Chapter 6 ). CI = confidence interval; POR = proportional odds ratio.

Figure 12.4.b Forest plot depicting standardized effect estimates (odds ratios) for satisfaction

Box 12.4.b How to describe the results from this structured summary

12.4.2 Overview of scenarios 2–4: synthesis approaches

We now address three scenarios in which review authors have decided that the outcomes reported in the 15 studies all broadly reflect satisfaction with care. While the measures were quite diverse, a synthesis is sought to help decision makers understand whether women and their birth partners were generally more satisfied with the care received in midwife-led continuity models compared with other models. The three scenarios differ according to the data available (see Table 12.4.c ), with each reflecting progressively less complete reporting of the effect estimates. The data available determine the synthesis method that can be applied.

• Scenario 2: effect estimates available without measures of precision (illustrating synthesis of summary statistics).
• Scenario 3: P values available (illustrating synthesis of P values).
• Scenario 4: directions of effect available (illustrating synthesis using vote-counting based on direction of effect).

For studies that reported multiple satisfaction outcomes, one result is selected for synthesis using the decision rules in Box 12.4.a (point 2).

Table 12.4.c Scenarios 2, 3 and 4: available data for the selected outcome from each study

* All scales operate in the same direction. Higher scores indicate greater satisfaction. ** For a particular scenario, the ‘available data’ column indicates the data that were directly reported, or were calculated from the reported statistics, in terms of: effect estimate, direction of effect, confidence interval, precise P value, or statement regarding statistical significance (either statistically significant, or not). CI = confidence interval; direction = direction of effect reported or can be calculated; MD = mean difference; NS = not statistically significant; OR = odds ratio; RD = risk difference; RoB = risk of bias; RR = risk ratio; sig. = statistically significant; SMD = standardized mean difference; Stand. = standardized.

12.4.2.1 Scenario 2: summarizing effect estimates

In Scenario 2, effect estimates are available for all outcomes. However, for most studies, a measure of variance is not reported, or cannot be calculated from the available data. We illustrate how the effect estimates may be summarized using descriptive statistics. In this scenario, it is possible to calculate odds ratios for all studies. For the continuous outcomes, this involves first calculating a standardized mean difference, and then converting this to an odds ratio ( Chapter 10, Section 10.6 ). The median odds ratio is 1.32 with an interquartile range of 1.02 to 1.53 (15 studies). Box-and-whisker plots may be used to display these results and examine informally whether the distribution of effects differs by the overall risk-of-bias assessment ( Figure 12.4.a , Panel A). However, because there are relatively few effects, a reasonable alternative would be to present bubble plots ( Figure 12.4.a , Panel B).

An example description of the results from the synthesis is provided in Box 12.4.c .

Box 12.4.c How to describe the results from this synthesis

12.4.2.2 Scenario 3: combining P values

In Scenario 3, there is minimal reporting of the data, and the type of data and statistical methods and tests vary. However, 11 of the 15 studies provide a precise P value and direction of effect, and a further two report a P value less than a threshold (<0.001) and direction. We use this scenario to illustrate a synthesis of P values. Since the reported P values are two-sided ( Table 12.4.c , column 6), they must first be converted to one-sided P values, which incorporate the direction of effect ( Table 12.4.c , column 7).

Fisher’s method for combining P values involved calculating the following statistic:

The combination of P values suggests there is strong evidence of benefit of midwife-led models of care in at least one study (P < 0.001 from a Chi 2 test, 13 studies). Restricting this analysis to those studies judged to be at an overall low risk of bias (sensitivity analysis), there is no longer evidence to reject the null hypothesis of no benefit of midwife-led model of care in any studies (P = 0.314, 3 studies). For the five studies reporting continuous satisfaction outcomes, sufficient data (precise P value, direction, total sample size) are reported to construct an albatross plot ( Figure 12.4.a , Panel C). The location of the points relative to the standardized mean difference contours indicate that the likely effects of the intervention in these studies are small.

An example description of the results from the synthesis is provided in Box 12.4.d .

Box 12.4.d How to describe the results from this synthesis

12.4.2.3 Scenario 4: vote counting based on direction of effect

In Scenario 4, there is minimal reporting of the data, and the type of effect measure (when used) varies across the studies (e.g. mean difference, proportional odds ratio). Of the 15 results, only five report data suitable for meta-analysis (effect estimate and measure of precision; Table 12.4.c , column 8), and no studies reported precise P values. We use this scenario to illustrate vote counting based on direction of effect. For each study, the effect is categorized as beneficial or harmful based on the direction of effect (indicated as a binary metric; Table 12.4.c , column 9).

Of the 15 studies, we exclude three because they do not provide information on the direction of effect, leaving 12 studies to contribute to the synthesis. Of these 12, 10 effects favour midwife-led models of care (83%). The probability of observing this result if midwife-led models of care are truly ineffective is 0.039 (from a binomial probability test, or equivalently, the sign test). The 95% confidence interval for the percentage of effects favouring midwife-led care is wide (55% to 95%).

The binomial test can be implemented using standard computer spreadsheet or statistical packages. For example, the two-sided P value from the binomial probability test presented can be obtained from Microsoft Excel by typing =2*BINOM.DIST(2, 12, 0.5, TRUE) into any cell in the spreadsheet. The syntax requires the smaller of the ‘number of effects favouring the intervention’ or ‘the number of effects favouring the control’ (here, the smaller of these counts is 2), the number of effects (here 12), and the null value (true proportion of effects favouring the intervention = 0.5). In Stata, the bitest command could be used (e.g. bitesti 12 10 0.5 ).

A harvest plot can be used to display the results ( Figure 12.4.a , Panel D), with characteristics of the studies represented using different heights and shading. A sensitivity analysis might be considered, restricting the analysis to those studies judged to be at an overall low risk of bias. However, only four studies were judged to be at a low risk of bias (of which, three favoured midwife-led models of care), precluding reasonable interpretation of the count.

An example description of the results from the synthesis is provided in Box 12.4.e .

Box 12.4.e How to describe the results from this synthesis

Figure 12.4.a Possible graphical displays of different types of data. (A) Box-and-whisker plots of odds ratios for all outcomes and separately by overall risk of bias. (B) Bubble plot of odds ratios for all outcomes and separately by the model of care. The colours of the bubbles represent the overall risk of bias judgement (green = low risk of bias; yellow = some concerns; red = high risk of bias). (C) Albatross plot of the study sample size against P values (for the five continuous outcomes in Table 12.4.c , column 6). The effect contours represent standardized mean differences. (D) Harvest plot (height depicts overall risk of bias judgement (tall = low risk of bias; medium = some concerns; short = high risk of bias), shading depicts model of care (light grey = caseload; dark grey = team), alphabet characters represent the studies)

12.5 Chapter information

Authors: Joanne E McKenzie, Sue E Brennan

Acknowledgements: Sections of this chapter build on chapter 9 of version 5.1 of the Handbook , with editors Jonathan J Deeks, Julian PT Higgins and Douglas G Altman.

We are grateful to the following for commenting helpfully on earlier drafts: Miranda Cumpston, Jamie Hartmann-Boyce, Tianjing Li, Rebecca Ryan and Hilary Thomson.

Funding: JEM is supported by an Australian National Health and Medical Research Council (NHMRC) Career Development Fellowship (1143429). SEB’s position is supported by the NHMRC Cochrane Collaboration Funding Program.

12.6 References

Achana F, Hubbard S, Sutton A, Kendrick D, Cooper N. An exploration of synthesis methods in public health evaluations of interventions concludes that the use of modern statistical methods would be beneficial. Journal of Clinical Epidemiology 2014; 67 : 376–390.

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Boonyasai RT, Windish DM, Chakraborti C, Feldman LS, Rubin HR, Bass EB. Effectiveness of teaching quality improvement to clinicians: a systematic review. JAMA 2007; 298 : 1023–1037.

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Bushman BJ, Wang MC. Vote-counting procedures in meta-analysis. In: Cooper H, Hedges LV, Valentine JC, editors. Handbook of Research Synthesis and Meta-Analysis . 2nd ed. New York (NY): Russell Sage Foundation; 2009. p. 207–220.

Crowther M, Avenell A, MacLennan G, Mowatt G. A further use for the Harvest plot: a novel method for the presentation of data synthesis. Research Synthesis Methods 2011; 2 : 79–83.

Friedman L. Why vote-count reviews don’t count. Biological Psychiatry 2001; 49 : 161–162.

Grimshaw J, McAuley LM, Bero LA, Grilli R, Oxman AD, Ramsay C, Vale L, Zwarenstein M. Systematic reviews of the effectiveness of quality improvement strategies and programmes. Quality and Safety in Health Care 2003; 12 : 298–303.

Harrison S, Jones HE, Martin RM, Lewis SJ, Higgins JPT. The albatross plot: a novel graphical tool for presenting results of diversely reported studies in a systematic review. Research Synthesis Methods 2017; 8 : 281–289.

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O’Brien MA, Rogers S, Jamtvedt G, Oxman AD, Odgaard-Jensen J, Kristoffersen DT, Forsetlund L, Bainbridge D, Freemantle N, Davis DA, Haynes RB, Harvey EL. Educational outreach visits: effects on professional practice and health care outcomes. Cochrane Database of Systematic Reviews 2007; 4 : CD000409.

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Schriger DL, Sinha R, Schroter S, Liu PY, Altman DG. From submission to publication: a retrospective review of the tables and figures in a cohort of randomized controlled trials submitted to the British Medical Journal. Annals of Emergency Medicine 2006; 48 : 750–756, 756 e751–721.

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ter Wee MM, Lems WF, Usan H, Gulpen A, Boonen A. The effect of biological agents on work participation in rheumatoid arthritis patients: a systematic review. Annals of the Rheumatic Diseases 2012; 71 : 161–171.

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How to Synthesize Written Information from Multiple Sources

Shona McCombes

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B.A., English Literature, University of Glasgow

Shona McCombes is the content manager at Scribbr, Netherlands.

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On This Page:

When you write a literature review or essay, you have to go beyond just summarizing the articles you’ve read – you need to synthesize the literature to show how it all fits together (and how your own research fits in).

Synthesizing simply means combining. Instead of summarizing the main points of each source in turn, you put together the ideas and findings of multiple sources in order to make an overall point.

At the most basic level, this involves looking for similarities and differences between your sources. Your synthesis should show the reader where the sources overlap and where they diverge.

Unsynthesized Example

Franz (2008) studied undergraduate online students. He looked at 17 females and 18 males and found that none of them liked APA. According to Franz, the evidence suggested that all students are reluctant to learn citations style. Perez (2010) also studies undergraduate students. She looked at 42 females and 50 males and found that males were significantly more inclined to use citation software ( p < .05). Findings suggest that females might graduate sooner. Goldstein (2012) looked at British undergraduates. Among a sample of 50, all females, all confident in their abilities to cite and were eager to write their dissertations.

Synthesized Example

Studies of undergraduate students reveal conflicting conclusions regarding relationships between advanced scholarly study and citation efficacy. Although Franz (2008) found that no participants enjoyed learning citation style, Goldstein (2012) determined in a larger study that all participants watched felt comfortable citing sources, suggesting that variables among participant and control group populations must be examined more closely. Although Perez (2010) expanded on Franz’s original study with a larger, more diverse sample…

Step 1: Organize your sources

After collecting the relevant literature, you’ve got a lot of information to work through, and no clear idea of how it all fits together.

Before you can start writing, you need to organize your notes in a way that allows you to see the relationships between sources.

One way to begin synthesizing the literature is to put your notes into a table. Depending on your topic and the type of literature you’re dealing with, there are a couple of different ways you can organize this.

Summary table

A summary table collates the key points of each source under consistent headings. This is a good approach if your sources tend to have a similar structure – for instance, if they’re all empirical papers.

Each row in the table lists one source, and each column identifies a specific part of the source. You can decide which headings to include based on what’s most relevant to the literature you’re dealing with.

For example, you might include columns for things like aims, methods, variables, population, sample size, and conclusion.

For each study, you briefly summarize each of these aspects. You can also include columns for your own evaluation and analysis.

The summary table gives you a quick overview of the key points of each source. This allows you to group sources by relevant similarities, as well as noticing important differences or contradictions in their findings.

Synthesis matrix

A synthesis matrix is useful when your sources are more varied in their purpose and structure – for example, when you’re dealing with books and essays making various different arguments about a topic.

Each column in the table lists one source. Each row is labeled with a specific concept, topic or theme that recurs across all or most of the sources.

Then, for each source, you summarize the main points or arguments related to the theme.

The purposes of the table is to identify the common points that connect the sources, as well as identifying points where they diverge or disagree.

Step 2: Outline your structure

Now you should have a clear overview of the main connections and differences between the sources you’ve read. Next, you need to decide how you’ll group them together and the order in which you’ll discuss them.

For shorter papers, your outline can just identify the focus of each paragraph; for longer papers, you might want to divide it into sections with headings.

There are a few different approaches you can take to help you structure your synthesis.

If your sources cover a broad time period, and you found patterns in how researchers approached the topic over time, you can organize your discussion chronologically .

That doesn’t mean you just summarize each paper in chronological order; instead, you should group articles into time periods and identify what they have in common, as well as signalling important turning points or developments in the literature.

If the literature covers various different topics, you can organize it thematically .

That means that each paragraph or section focuses on a specific theme and explains how that theme is approached in the literature.

Source Used with Permission: The Chicago School

If you’re drawing on literature from various different fields or they use a wide variety of research methods, you can organize your sources methodologically .

That means grouping together studies based on the type of research they did and discussing the findings that emerged from each method.

If your topic involves a debate between different schools of thought, you can organize it theoretically .

That means comparing the different theories that have been developed and grouping together papers based on the position or perspective they take on the topic, as well as evaluating which arguments are most convincing.

Step 3: Write paragraphs with topic sentences

What sets a synthesis apart from a summary is that it combines various sources. The easiest way to think about this is that each paragraph should discuss a few different sources, and you should be able to condense the overall point of the paragraph into one sentence.

This is called a topic sentence , and it usually appears at the start of the paragraph. The topic sentence signals what the whole paragraph is about; every sentence in the paragraph should be clearly related to it.

A topic sentence can be a simple summary of the paragraph’s content:

“Early research on [x] focused heavily on [y].”

For an effective synthesis, you can use topic sentences to link back to the previous paragraph, highlighting a point of debate or critique:

“Several scholars have pointed out the flaws in this approach.” “While recent research has attempted to address the problem, many of these studies have methodological flaws that limit their validity.”

By using topic sentences, you can ensure that your paragraphs are coherent and clearly show the connections between the articles you are discussing.

As you write your paragraphs, avoid quoting directly from sources: use your own words to explain the commonalities and differences that you found in the literature.

Don’t try to cover every single point from every single source – the key to synthesizing is to extract the most important and relevant information and combine it to give your reader an overall picture of the state of knowledge on your topic.

Step 4: Revise, edit and proofread

Like any other piece of academic writing, synthesizing literature doesn’t happen all in one go – it involves redrafting, revising, editing and proofreading your work.

Checklist for Synthesis

•   Do I introduce the paragraph with a clear, focused topic sentence?
•   Do I discuss more than one source in the paragraph?
•   Do I mention only the most relevant findings, rather than describing every part of the studies?
•   Do I discuss the similarities or differences between the sources, rather than summarizing each source in turn?
•   Do I put the findings or arguments of the sources in my own words?
•   Is the paragraph organized around a single idea?
•   Is the paragraph directly relevant to my research question or topic?
•   Is there a logical transition from this paragraph to the next one?

Further Information

How to Synthesise: a Step-by-Step Approach

Help…I”ve Been Asked to Synthesize!

Learn how to Synthesise (combine information from sources)

How to write a Psychology Essay

Case studies synthesis: a thematic, cross-case, and narrative synthesis worked example

• Published: 03 August 2014
• Volume 20 , pages 1634–1665, ( 2015 )

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• Daniela S. Cruzes 1 ,
• Tore Dybå 1 ,
• Per Runeson 2 &
• Martin Höst 2  

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Case studies are largely used for investigating software engineering practices. They are characterized by their flexible nature, multiple forms of data collection, and are mostly informed by qualitative data. Synthesis of case studies is necessary to build a body of knowledge from individual cases. There are many methods for such synthesis, but they are yet not well explored in software engineering. The objective of this research is to demonstrate the similarities and differences of the results and conclusions when applying three different methods of synthesis, and to discuss the challenges of synthesizing evidence from reported case studies in SE. We describe a worked example of three such methods where three independent teams synthesized two studies that investigated critical factors of trust in outsourced projects through thematic synthesis and cross-case analysis, and compared these to each other and also to an already published narrative synthesis. In addition, despite that the primary studies were well presented for synthesis, we identified challenges in the use of case studies synthesis methods related to the goals and research questions of the synthesis, the types and number of case studies, variations in context, limited access to raw data, and quality of the case studies. Thus, we recommend that the analysts should be aware of these challenges and try to account for them during the execution of the synthesis. We also recommend that analysts consider using more than one method of synthesis for sake of reliability of the results and conclusions.

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Cruzes, D.S., Dybå, T., Runeson, P. et al. Case studies synthesis: a thematic, cross-case, and narrative synthesis worked example. Empir Software Eng 20 , 1634–1665 (2015). https://doi.org/10.1007/s10664-014-9326-8

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• Published: 27 April 2024

Synthesis of CuO, ZnO nanoparticles, and CuO-ZnO nanocomposite for enhanced photocatalytic degradation of Rhodamine B: a comparative study

• M. Jeevarathinam 1 &
• I. V. Asharani 1  

Scientific Reports volume  14 , Article number:  9718 ( 2024 ) Cite this article

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• Environmental chemistry

Water pollution, arising from the presence of toxic dyes and chemicals, is a global challenge, urging the need for eco-friendly solutions in water decontamination. This study focused on the synthesis of copper oxide nanoparticles (CuO NPs), zinc oxide nanoparticles (ZnO NPs), and a bimetallic CuO-ZnO nanocomposite (CZ NC) through an environmentally friendly method employing Tragia involucrata L. leaf extract. Comprehensive analysis of structural and optical properties involved using various analytical techniques such as XRD, FT-IR, XPS, UV-DRS, PL, FE-SEM, EDAX, TEM, SAED, zeta potential, TGA, and BET. In comparison to pristine CuO and ZnO NPs, the CZ-NC demonstrated notably enhanced photocatalytic activity in the degradation of Rhodamine B dye (RhB). The optimum conditions for RhB degradation were found to be a pH of 9 and a catalyst dosage of 1 mg/mL for a concentration of 10 ppm. Under these conditions, CuO NPs, ZnO NPs, and CZ-NC demonstrated high efficiencies of 78%, 83%, and 96.1% respectively over 105 min. Through LC-HRMS, the identification of degradation products offered valuable insights into the pathway of photocatalytic degradation. Furthermore, toxicity analysis of intermediates, conducted through ECOSAR software, indicated the formation of non-toxic by-products (ChV/LC 50 /EC 50  > 100) after the completion of the reaction. Furthermore, the recycled catalysts exhibited sustained stability for up to 4 cycles, with only a minor decrease in activity of up to 6.8%. This confirms their catalytic efficacy in purifying polluted water. This research significantly contributes to the progress of environmentally friendly nanocomposites, enhancing their efficacy in the realm of environmental remediation.

Introduction

Ensuring the quality of water sources is essential for human life and the overall well-being of Earth's ecosystems. However, changing human lifestyles and industrial expansion have led to water and air pollution, resulting in various health challenges. Recently, increasing environmental concern has focused on hazardous pollutants, specifically dyes originating from the textile industry 1 , 2 . The widespread use of dyes in textiles has made them significant contributors to aquatic pollution, posing threats to both aquatic life and human health due to their non-biodegradable and carcinogenic properties. Based on recent survey findings, it has been projected that a significant portion, approximately 65%, of the global population will be adversely affected by a scarcity of clean drinking water by the year 2050 2 . Several synthetic organic dyes, such as the xanthene-based Rhodamine B dye commonly used in textiles, display high solubility and possess the potential to induce organ inflammation in living organisms 3 . This toxic textile dye was used as a hue additive in cotton candy and food items and was recently banned by India’s Tamil Nadu government, which could be harmful to people’s health. Despite extensive research efforts, conventional methods such as biological oxidation, adsorption, coagulation, precipitation, and filtration have shown limited efficacy in treating dye pollutants and may produce toxic compounds during the treatment process 4 . Presently, chemical approaches are utilized for treating dye contaminants; however, their application is restricted by significant chemical consumption, pH sensitivity, and the generation of hazardous by-products, including carcinogenic aromatic amines, resulting in increased operational costs 5 . Similarly, electrocoagulation, a prevalent technique for achieving heightened pollution degradation rates, has drawbacks, encompassing rapid depletion of sacrificial anodes, necessitating frequent replacements, potential sludge generation, and diminished treatment efficiency attributed to electrode passivation 6 .

Recent studies indicate the effectiveness of advanced oxidation processes (AOP) such as sonolysis, ionizing radiation, Photo-Fenton, ozonation, and photocatalysis as different methods for removing pollutants from water 7 . Employing metal oxides in photocatalytic methods for breaking down dye pollutants relies on the generation of hydroxyl radicals ( • OH) and superoxide radicals ( • O 2 - ) during the decomposition process. This leads to the production of non-toxic by-products such as water and carbon dioxide, ensuring the success of the remediation process 8 , 9 , 10 . The photocatalytic process is a viable approach for the removal of pollutants from water, presenting a cost-effective solution to address environmental contamination 11 . The photocatalytic effectiveness of metal NPs is influenced by various factors, including particle size and shape 12 , 13 , surface modifications 14 , 15 and surface area 16 . The synthesis of nanoparticles, accomplished through methods such as chemical reduction, co-precipitation, sol–gel processes, and environmentally friendly approaches involving biological agents, provides precise control over size, shape, and surface properties 17 . These versatile nanoparticles find applications in medicine for drug delivery, imaging, and diagnostics, as well as in catalysis, electronics, and environmental remediation 18 . Continued research is exploring their utilization in targeted drug delivery and innovative therapeutic approaches in medicine. This not only contributes to smaller and more efficient components in electronics but also enhances solar cell efficiency and enables environmental remediation 19 . Nanomaterials' multifaceted contributions continue to shape innovations across diverse scientific disciplines 20 .

Distinguishing themselves from various transition metal oxide counterparts, copper oxide (CuO) nanoparticles exhibit commendable catalytic and conductivity properties, characterized by p-type conducting behavior. This behavior stems from their lower bandgap of 1.7 eV, efficient electron transport, non-toxicity, and the presence of a higher number of active sites within their monoclinic structure 21 , 22 . These attributes find applications across diverse fields, including sensors, optoelectronics, magneto-electronics, and biomedical applications. Bimetallic nanoparticles (NPs) have gained increased attention for their superior properties, driving innovative applications that outperform their monometallic counterparts 23 . In contrast to monometallic alternatives, bimetallic systems offer unique advantages, demonstrating enhancements and modifications across various characteristics. The integration of mechanical, functional, electronic, and structural adjustments results in a synergistic effect when incorporating two disparate metals 24 . These interactions activate controlled optical, thermal, magnetic, plasmonic, and electrical features, significantly broadening their functionalities and applications in catalysis 25 . The improved attributes of bimetallic systems suggest a more effective pathway toward sustainable advancements.

In the realm of photocatalysis, the significance of bimetallic NPs lies in their efficient electron transport between the valence and conduction bands, leading to the generation of more radicals. Incorporating secondary transition metal oxides into CuO NPs has improved the properties of pure copper oxides, affecting the bandgap, conductivity, electrocatalytic, and photocatalytic activity 21 , 26 .

To further enhance these characteristics, an n-type ZnO semiconductor was chosen for its high chemical stability, outstanding photostability, and widespread availability in society 27 , 28 . The incorporation of ZnO into CuO nanoparticles creates additional active sites for catalytic activity and suppresses the recombination of electron–hole pairs 29 .

Various methods are employed for synthesizing metal oxide (MO) NPs, categorized as either bottom-up or top-down approaches. Increasing attention is being given to non-toxic, environmentally friendly green synthesis methods that utilize naturally available plants for NPs synthesis 30 . Plant extracts, abundant in phytochemicals, play a crucial role in both the reduction and the stabilization of NPs. Previous studies indicate that the use of plant extracts for metal NPs synthesis is a more dependable approach compared to other biogenic methods 31 . As an example, Aragaw et al. employed Eichhornia crassipes plant extract to synthesize a p-Co 3 O 4 /n-ZnO heterojunction photocatalyst, successfully degrading methylene blue dye 32 . Another study by M. Ahmad et al. utilized Carya illinoinensis leaf extract to fabricate ZnO and Au-decorated ZnO NPs for the photocatalytic degradation of Rhodamine B 33 .

In this investigation, we have utilized Tragia involucrata L known as "Senthatti” for the first time to synthesize CuO NPs, ZnO NPs, and a copper oxide-zinc oxide nanocomposite (CZ-NC). This plant species is renowned for its rich phytochemical composition, encompassing alkaloids, flavonoids, tannins, saponins, steroids, and carbohydrates 34 , 35 , 36 . Therefore, we are fascinated to explore the potential of plant material to synthesize different nanomaterials and study their photocatalytic efficiency against the degradation of RhB, extensively used in the textile and paint industries, leading to significant water contamination due to its toxic and non-biodegradable characteristics. The effect of various parameters on the degradation of RhB was also studied in the presence of CZ-NC. The toxicity of the products resulting from the photocatalytic degradation of RhB was analyzed using the ECOSAR software. The produced products are less toxic compared to the parent dye molecules. A comparison between the present study and the previously reported results on photocatalytic RhB dye degradation is also presented.

Experimental methods

Copper nitrate trihydrate (Cu(NO 3 ) 2 ·3H 2 O) and zinc nitrate hexahydrate (Zn(NO 3 ) 2 ·6H 2 O) were acquired from Sisco Research Laboratory in India. RhB dye (C 28 H 31 N 2 O 3 Cl) was purchased from Central Drug House Pvt Ltd, India. Sodium hydroxide (NaOH) and hydrochloric acid (HCl) were obtained from Molychem, India. Acetonitrile (CH 3 CN) of HPLC grade was supplied by SD Fine Chem Ltd, India. The chemicals were used without additional purification, and double-distilled (DD) water was employed in the NPs preparation process. Tragia involucrata L. leaf was locally collected in the Arakkonam region of Tamil Nadu.

Preparation of Tragia involucrata L. leaf extract

The Botanical Survey of India recorded the green source of Tragia involucrata L. at Coimbatore (BSI/SRC/5/23/2021/Tech-176). The plant collection and usage adhered to all relevant guidelines. The Tragia involucrata L . leaves were cleaned with deionized water to remove dust and then dried in a dark place at room temperature for 7 days before being crushed into a powder. Subsequently, 10 g of the powdered leaves was heated in 100 mL of deionized water for 2 h at 80 °C and filtered. The resulting green solution was then stored in the refrigerator.

Synthesis of photocatalysts

To synthesize the CuO-ZnO nanocomposite (CZ-NC), 2.41 g of Cu(NO 3 ) 2 ·3H 2 O and 2.97 g of Zn(NO 3 ) 2 ·6H 2 O precursors were dissolved in 80 mL of water, and 20 mL of TI leaf extract was added. The mixture was then heated at 80 °C with constant stirring. The pH of the solution was adjusted to 12 using NaOH. After 2 h, the green solution transitioned to a brown color, indicating the successful formation of CZ-NC. The resulting mixture was centrifuged to collect the precipitate, which underwent thorough washing with DD water to remove impurities. Subsequently, the nanocomposite was subjected to calcination at 400 °C to eliminate plant debris. In parallel, pristine CuO NPs and pristine ZnO NPs were prepared separately using the same procedure, utilizing Cu(NO 3 ) 2 ·3H 2 O precursor for CuO NPs and Zn(NO 3 ) 2 ·6H 2 O for ZnO NPs. The plant extract plays a vital role in nanoparticle synthesis, serving as both a robust reducing and stabilizing agent. Polyphenolic groups, directly tied to the number of hydroxyl groups, contribute to the extract's reduction potential. Notably, flavonoids within the extract exhibit reduction potentials ranging from 0.119 to 1.021 V 37 .

Figure  1 illustrates a schematic diagram of the interaction between plant extract components and metal ion precursors, elucidating the mechanism of nanoparticle formation. The distinct reduction potentials of CuO (0.34 V) and ZnO (-0.76 V) highlight the redox compatibility governing the efficiency of the reduction process 38 .

A possible mechanism for the synthesis of nanomaterials.

Characterization methods

The synthesized catalyst underwent thorough characterization, including X-ray diffractometry (XRD) using X’–Pert Pro for structural analysis and Fourier-transform infrared spectroscopy (FT-IR) with PerkinElmer to identify functional groups. X-ray photoelectron spectroscopy (XPS) from Thermo Fisher revealed chemical states and elements, while UV–visible (UV–Vis) spectra with JASCO V-670 PC were employed to monitor CZ-NC formation. Scanning Electron Microscopy (SEM) with FEI-Tecnai G2 20 Twin was used to analyze the surface characteristics of CuO and ZnO NPs, and high-resolution transmission electron microscopy was utilized to examine the surface structure, size, and compositions of CZ-NC. The Hitachi F7000 spectrofluorometer was used to estimate the recombination (e − /h + ) performance of the synthesized catalysts. Zeta potential values (Horiba scientific SZ-100) provided insights into stability and surface charge. The pore size and surface area were analyzed using the Brunauer–Emmett–Teller (BET) technique with AutosorbiQ from Quantachrome USA. Ultra Performance Liquid Chromatography (UPLC) from WATERS SM-FTN ACQUITY H-CLASS PDA sensor monitored degradation over time, and Liquid Chromatography-High-Resolution Mass Spectrometry (LC-HRMS) with WATERS–XEVO G2-XS-QToF identified photocatalytic degradation products.

Photocatalytic dye degradation

The photocatalytic efficiency of the prepared catalysts was assessed by degrading RhB dye (10 ppm) in a UV-light-irradiated photoreactor at room temperature. A mercury vapor lamp (365 nm, 250 W) served as the light source. Initially, a 50 mL RhB dye solution (10 ppm) was stirred with the catalyst (1 mg/mL) for 30 min to attain adsorption and desorption equilibrium. The resulting mixture was then transferred to a quartz tube and exposed to light irradiation under a mercury vapor lamp in an annular-type photoreactor, with continuous stirring facilitated by an air pump. The aliquots (2 mL) were collected at regular time intervals, filtered to remove the catalyst, and analyzed for absorbance using a UV–Vis spectrophotometer. After complete degradation, the catalyst was separated from the solution, dried, and reused following an ethanol-washing treatment. To elucidate the dye degradation mechanism, a similar reaction was conducted with different scavengers such as ethylenediaminetetraacetic acid (EDTA), p-benzoquinone (PBQ), and terephthalic acid (TPA). The percentage (%) of degradation was determined to assess the impact of these scavengers.

Results and discussion

Powder xrd analysis.

Figure  2 depicts the powder XRD pattern of the prepared CuO NPs, ZnO NPs, and the CZ-NC. XRD analysis aimed to elucidate the structural properties of the prepared nanomaterials. In Fig.  2 , the peaks corresponding to CuO and ZnO NPs are noticeable and individually labelled as CuO and ZnO NPs. Concerning CuO NPs, the diffraction peaks at 2θ angles of 35.5°, 38.7°, 48.7°, 53.4°, 58.3°, 61.5°, 66.2°, and 68.1° were identified as reflection planes (110), (111), (-202), (020), and (202) 39 . These crystalline planes strongly confirm the monoclinic structure of CuO NPs, corroborated by comparison with JCPDS No. 05–0661. For ZnO NPs, peaks were observed at 31.7°, 34.4°, 36.2°, 47.5°, 56.6°, 62.8°, and 69.0°, corresponding to (100), (002), (101), (102), (110), (103), and (201) planes 40 . These planes confirm the hexagonal structure of ZnO, consistent with JCPDS No. 036–1451. Furthermore, the XRD pattern indicates the presence of a two-phase CuO-ZnO nanocomposite with no observable impurities. The sharp peaks in the diffractogram suggest the crystallinity of the prepared CZ-NC. The observed pattern aligns with previously reported results, affirming the reliability of the synthesis process 41 , 42 . The CZ-NC exhibited an average crystallite size of 20.2 nm. In comparison, pristine ZnO and CuO NPs showed average crystallite sizes of 23.3 and 24.1 nm, respectively. Additionally, the presence of two distinct phase structures in the nanocomposite indicates the separate formation of CuO and ZnO NPs. The lattice parameters were calculated and given in Table S1 .

Powder XRD analysis of CuO NPs, ZnO NPs and CZ-NC.

FT-IR analysis

FT-IR analysis was carried out to identify chemical functional groups associated with the reducing and capping processes during the synthesis of the materials. Figure  3 displays the FT-IR results of Tragia involucrata L . (TI) leaf extract, CuO NPs, ZnO NPs, and the CZ-NC, respectively. The functional groups play a crucial role in nanomaterials preparation. Specifically, in TI, the wavenumbers 3385 cm -1 and 2973 cm -1 were associated with the O–H functional group and C-H alkane, respectively. The bands at 1646 and 1377 cm -1 were attributed to C = O stretching and O–H bending vibrations, respectively. Peaks at 1045, 874, and 668 cm -1 corresponded to C–O stretching, C–H, and C = C bending vibrations, respectively. These peaks indicate the presence of alkaloids, flavonoids, tannins, saponins, terpenoids, steroids, and carbohydrates in the TI extract 36 . These phytoconstituents' functional groups help in the reduction and stabilization of the nanomaterials. In Fig.  3 b-d, the bands at 513 and 540 cm -1 correspond to Cu–O and Zn–O stretching vibrations, respectively. The peaks at 980 and 478 cm -1 indicate the formation of CZ-NC 43 , 44 .

FT-IR spectra of ( a ) TI aqueous leaf extract, ( b ) CuO NPs, ( c ) ZnO NPs and ( d ) CZ-NC.

XPS analysis

XPS analysis was performed to ascertain the elemental states of the synthesized CZ-NCs. The wide survey spectrum in Fig.  4 a reveals the presence of copper (Cu-2p), oxygen (O-1s), zinc (Zn-2p), and carbon (C-1s) atoms in the CZ-NC, with the carbon signal attributed to the plant extract. In Fig.  4 b, the XPS of Cu exhibits peaks at 933.8 and 953.6 eV, corresponding to Cu (2p 3/2 ) and Cu (2p 1/2 ), respectively. The confirmation of Cu atoms in the Cu 2+ state is evident from satellite peaks at binding energies 941.5 eV and 943.2 eV for Cu (2p 3/2 ), along with 962 eV for Cu (2p 1/2 ) 45 , 46 , 47 , 48 . Similarly, Fig.  4 c displays peaks at binding energies of 1021.9 eV and 1045 eV, indicating the orbitals with doublets 2p 3/2 and 2p 1/2 , respectively. Additionally, in Fig.  4 d, the peak at a binding energy of 530 eV signifies the electron orbital state of O 1s resulting from the oxygen lattice (O 2- ) bonding with metal ions 49 . The peak at a binding energy of 531.5 eV is attributed to O(vac), and the binding energy at 532.3 eV indicates the surface with O-containing groups such as H 2 O, OH, and O 2- 41 , 46 . The XPS survey reveals the presence of Cu 2+ O 2- and Zn 2+ O 2- with specific chemical compositions and states.

XPS spectra of CZ-NC. ( a ) Overall survey scan, ( b ) Cu 2p, ( c ) Zn 2p and ( d ) O 1 s.

Optical properties

The optical bandgap analysis was conducted to comprehend the optical properties of the synthesized compounds. Figure  5 a presents the UV-DRS spectra of ZnO NPs, CuO NPs, and the CZ-NC. The presence of CuO and ZnO NPs is confirmed by the broad absorbance observed in the wavelength range of 200 to 800 nm. The peak observed in the UV-DRS spectrum of the CZ-NC indicates absorption at a wavelength with a red shift towards a longer wavelength region. This shift is associated with the formation of CuO and the incorporation of ZnO, influenced by the smaller band gap of CuO compared to ZnO and the quantum size effect. This shift in absorption wavelength reaffirms the successful formation of the CZ-NC 41 , 50 . In Fig.  5 b, the band gap values for the synthesized CuO NPs, ZnO NPs, and CZ-NC were determined from a Tauc plot.

( a ) UV–Vis DRS of CuO, ZnO NPs and CZ-NC, ( b ) Tauc plot of CuO, ZnO NPs and CZ-NC, ( c ) PL spectra of CuO, ZnO NPs and CZ-NC.

The absorption coefficient (α), frequency (ν), and γ (indicating whether transitions are direct or indirect, where γ = 2 for direct allowed transitions, γ = 2/3 for direct forbidden transitions, γ = 1/2 for indirect allowed transitions, and γ = 1/3 for indirect forbidden transitions) were considered in determining the energy band gap (E g ). The calculated energy band gap between the energy bands of the CZ-NC was found to be 2.40 eV. In contrast, CuO NPs and ZnO NPs exhibited energy band gap values of 1.40 eV and 3.08 eV, respectively. This suggests a significantly higher rate of electron transfer in the CZ-NC, potentially leading to increased photocatalytic efficiency.

To examine the process of photogenerated charge carrier (e - and h + ) separation and recombination, photoluminescence (PL) measurements were taken using an excitation wavelength of 375 nm (Fig.  5 c). An intense peak in PL signifies rapid recombination of charge carriers, while a subdued PL intensity indicates a slower recombination probability 41 . CZ-NC displays the least PL intensity compared to other synthesized materials. This indicates that the recombination rate of photogenerated e - and h + is significantly impeded in CZ-NC, suggesting potentially higher photocatalytic activity than CuO NPs and ZnO NPs.

SEM and EDX

The surface characteristics of the synthesized photocatalysts are depicted in Figs.  6 a-c. The morphologies of CuO NPs and ZnO NPs are shown in Figs.  6 a and 6b, revealing distinctive irregular-sized spherical structures on their surfaces. Figure  6 c illustrates the combination of CuO and ZnO, indicating that this combination does not alter the overall structure of the nanocomposite particles. Since zinc ions are incapable of oxidizing copper, the resulting oxides are obtained individually rather than forming a bimetallic oxide. Moreover, the observed particles exhibit homogeneity, suggesting a similar morphology for CuO and ZnO NPs. Notably, CZ-NC demonstrates a higher surface roughness compared to CuO and ZnO NPs. In contrast to pristine CuO and ZnO NPs, the surface of CZ-NC exhibits agglomeration (Fig.  6 c), indicating the higher surface energy of CZ-NC 50 .

( a , d ) SEM and EDX image of CuO NPs, ( b , e ) ZnO NPs and ( c , f ) CZ-NC.

Figures  6 d-f display the EDX spectra, confirming the presence of copper (Cu), zinc (Zn), and oxygen (O) elements with their respective atomic percentages in the synthesized catalyst. Due to its higher reduction potential (0.34) compared to zinc (-0.76), copper undergoes more reduction, resulting in higher atomic ratios in the prepared CZ-NC 38 .

The TEM results, depicted in Figs.  7 a, 7c, and 7e provide insights into the shape and size characteristics of CuO NPs, ZnO NPs, and CZ-NC. All three—CuO NPs, ZnO NPs, and CZ-NC—display spherical shapes with well-separated particles, maintaining a uniform appearance. This observation is consistent with findings from previous studies where the plant extract-mediated synthesis of nanoparticles resulted in a spherical morphology, demonstrating excellent photocatalytic activity 51 . Through histogram analysis, the calculated particle sizes are determined to be 46 nm for CuO NPs, 34 nm for ZnO NPs, and 39 nm for CZ-NC. The selected area electron diffraction (SAED) patterns in Figs.  7 b and 7d reveal lattice plane rings corresponding to CuO NPs and ZnO NPs, displaying successive concentric circles that indicate their highly crystalline nature. In Fig.  7 f, the SAED pattern of CZ-NCs shows spots indexed as CuO (111), (-202), and (110), and ZnO (101), (102), and (101). These indexed diffraction rings suggest the presence of CuO with a monoclinic structure and ZnO with a hexagonal structure. The observation of spherical particle shapes, ring patterns, and distinct spots collectively signifies the semi-crystalline nature of the synthesized CZ-NC, consistent with the findings from XRD results 43 , 52 .

TEM images and SAED patterns of ( a , b ) CuO NPs, ( c , d ) ZnO NPs and ( e , f ) CZ-NC.

Zeta potential and BET analysis

Figures  8 a-c showcase the results of zeta potential (ZP), providing insights into the surface charges of CuO NPs, ZnO NPs, and CuO-ZnO NCs. The recorded ZP values are -42.6, -26.4, and -44.8 mV for CuO NPs, ZnO NPs, and CZ-NCs, respectively. These values imply electrical repulsion, preventing particle aggregation and ensuring improved stability 53 . The negative ZP indicates the presence of negatively charged ions on the surfaces of NPs, contributing to the enhanced degradation of RhB dye. This is particularly noteworthy as RhB is a cationic dye, promoting better interaction between the dye and the catalyst 54 , 55 .

( a – c ) Zeta potential analysis of CuO NPs, ZnO NPs and CZ-NC and ( d – f ) BET analysis of CuO NPs, ZnO NPs and CZ-NC.

The N 2 adsorption–desorption isotherms for CuO NPs, ZnO NPs, and CZ-NC are illustrated in Figs.  8 d-f. This analytical approach is crucial for understanding key factors that influence photocatalytic activity, particularly the direct adsorption of dye onto the catalyst surface. The N 2 adsorption–desorption isotherm of the nanocomposite exhibits a type IV isotherm characterized by narrow H 3 -type hysteresis loops. The respective surface areas of CuO NPs, ZnO NPs, and CZ-NC were determined to be 7.801, 22.387, and 8.017 m 2 /g. Furthermore, the porosity, as evaluated using the Barrett–Joyner–Halenda (BJH) pore size, was measured at 1.424, 1.420, and 2.028 nm for CuO NPs, ZnO NPs, and CZ-NC, respectively. These results suggest that, compared to CuO and ZnO NPs, CZ-NC exhibits a mesoporous nature with the potential to significantly enhance photocatalytic efficiency 56 , 57 .

Photocatalytic experiment

Photocatalytic rhb degradation.

The photocatalytic efficiency of CuO NPs, ZnO NPs, and CZ-NC was assessed by degrading RhB dye under ultraviolet light exposure over 105 min, as depicted in Fig.  9 . Regular monitoring of the degradation process using a UV–Vis spectrophotometer revealed a decrease in absorbance intensity at the wavelength of 554 nm. Using Eq. ( 3 ), the calculated degradation efficiencies were 78%, 83%, and 96.1% for CuO NPs, ZnO NPs, and CZ-NC, respectively. It may be due to the combined effect of CuO and ZnO in the CZ-NC which significantly enhanced the degradation of RhB. Importantly, in the absence of a catalyst in the reaction, the degradation of RhB was not observed. Hence, the influence of various parameters on RhB dye degradation was investigated exclusively in the presence of CZ-NC. This exploration involved altering the concentration of RhB, the catalyst, and the pH of the reaction mixture.

where, C o —is the initial absorbance, C t —the final absorbance after degradation. The reaction rate was calculated using the relation followed by (4).

Kinetics of degradation of RhB dye by CZ-NC.

Influence of pH

The catalytic process is notably affected by the pH of the dye solution, a pivotal factor in modifying carriers on the catalyst surface and influencing interactions with hazardous contaminants. To examine the pH effects on the degradation of RhB (10 ppm) in the presence of CZ-NC (1 mg/mL), the pH of the dye solution was maintained as 5, 7, 9, and 11 respectively through the addition of HCl and NaOH. As depicted in Figs.  10 a and d, the relationship between pH and the degradation of RhB dye in the existence of the catalyst under ultraviolet light is evident. The degradation percentages at pH 5, 7, 9, and 11 were recorded as 47.19%, 82%, 96.1%, and 86.2%, respectively, as outlined in Table 1 . RhB degradation was limited to pH 5, it may be due to the repulsion between the catalyst and the dye caused by the presence of RhB + ions. Conversely, RhB degradation increased with higher pH values, likely attributed to the deprotonation of RhB + ions at basic pH, resulting in the formation of a Zwitter ion. However, at pH 11, degradation decreased, possibly due to an excess of OH - ions covering the catalyst surface, creating a negatively charged surface that repelled the reaction mixture 58 , 59 . Therefore, the optimal pH value for RhB dye degradation was determined to be 9.

Kinetics plot of degradation of RhB dye at ( a ) different pH, ( b ) different catalyst dosages, ( c ) different concentrations of RhB. The degradation percentage of RhB dye at ( d ) different pH, ( e ) different catalyst dosages, and ( f ) different concentrations of dye.

Effect of catalyst dosage

Similarly, the impact of catalyst dosage on dye degradation was investigated. Different solutions were prepared with varying catalyst dosages of 0.25, 0.50, 1, and 1.5 mg/mL while maintaining a constant RhB concentration of 10 ppm at pH 9. The observed degradation percentages for the dye were 64.06%, 76.5%, 96.1%, and 63.5%, respectively. Figure 10b,e display the influence of catalyst dosage on the photocatalytic dye degradation process. The results indicate an escalation in dye degradation with an increasing dosage of active catalyst sites. However, a decline in degradation is noted at a 1.5 mg/mL catalyst dosage due to light dispersion and reduced light interaction caused by the higher catalyst quantity, resulting in a more turbid mixture. Additionally, the catalyst surface may aggregate, becoming less accessible to photon absorption, potentially diminishing degradation efficiency. Consequently, the optimal catalyst dosage for RhB dye degradation was found to be 1 mg/mL.

Effect of concentration of dye

The experiments were conducted separately at varying concentrations of the dye (5, 10, and 15 ppm), with a constant catalyst dosage of 1 mg/mL and a pH of 9. In Fig. 10c,f, the photocatalytic degradation of RhB dye is depicted, showing degradation percentages of 100%, 96.1%, and 44.69% at concentrations of 5, 10, and 15 ppm, respectively. The corresponding rate constants are detailed in Table 1 . The observed trend indicates a decrease in the reaction rate as the dye concentration increases. This is attributed to the elevated amount of dye adsorbed on the catalyst surface, limiting both catalyst efficiency and light absorption on its surface.

Effect of scavengers on the reactive species and mechanism of photocatalytic activity

To identify the reactive species responsible for the degradation process, similar reactions were conducted using various scavengers such as ethylenediamine tetra-acetic acid (EDTA), p-benzoquinone (PBQ), and terephthalic acid (TPA). Each reaction was carried out separately with different scavengers: EDTA for h + , PBQ for • O 2 - , and TPA for • OH. This approach aimed to assess the specific reactive components involved in the degradation of RhB. Additionally, a blank reaction was performed without any scavengers to understand the impact of scavengers on the reactions. In Fig.  11 , the degradation rate was found to be 96.1% without the addition of any scavenger (blank). In the presence of scavengers, the degradation rates were 15.38%, 7%, and 29.57% for EDTA, PBQ, and TPA, respectively. This suggests that • O 2 is a crucial reactive species for the degradation reaction, although h + and • OH species may also play significant roles in RhB degradation.

The degradation efficiency of RhB with scavengers.

According to the results, the following mechanism can be derived for the photocatalytic RhB dye degradation.

The band edge position of the valence band (VB) conduction band (CB) potential of the synthesized photocatalyst can be theoretically calculated according to the following equation.

The catalyst's valence band (E VB ) and conduction band (E CB ) were identified, with E e representing the energy of free electrons on the hydrogen scale, fixed at 4.5 eV vs. NHE. When evaluating CuO and ZnO NPs, each with electronegativity values of 5.81 eV and 5.79 eV, respectively, the considered parameters included the band gap energy (E g ) and X, the geometric mean of Mulliken electronegativity 60 .

The calculated energy bandgaps for CuO and ZnO NPs revealed E VB and E CB values of 2.01 eV and 0.61 eV for CuO, and 2.83 eV and -0.25 eV for ZnO, respectively (refer to the Supplementary Material for detailed information). In Fig.  12 , the band alignments of CuO and ZnO are depicted both before and after the formation of the nanocomposite. Initially, the conduction band edge and Fermi level of CuO are lower than those of ZnO. As the CuO-ZnO nanocomposite evolves, a discernible shift in Fermi levels occurs. Specifically, the Fermi level of CuO rises, and that of ZnO decreases until equilibrium is achieved, as illustrated in Fig.  12 b after contact. This shift is attributed to the transfer of electrons between p-type CuO and n-type ZnO elements. During this electron transfer, ZnO loses electrons, generating a depletion layer on its surface and resulting in a positive shift in the Fermi level. Conversely, CuO gains electrons, leading to a negative shift in its Fermi level. Ultimately, the Fermi levels of both semiconductors equalize. Simultaneously, as these Fermi level adjustments occur, the entire energy band of ZnO lowers, while that of CuO rises 61 . Consequently, in the nanocomposite, CuO possesses a higher conduction band edge than ZnO. Utilizing the equations, experimental results, and information from reported articles, the energy band diagram of the CuO-ZnO nanocomposite is schematically presented in Fig.  12 .

Schematic representation of energy band diagram of CZ-NC ( a ) Before contact and ( b ) after contact of the CZ-NC catalyst.

Degraded RhB dye analysis

Uplc analysis.

The degradation of RhB dye was investigated using UPLC with a 60:40 v/v ratio of water (H 2 O) and acetonitrile (ACN) as the mobile phase. The analysis was conducted at different time intervals (0, 35, 75, 105 min) on RhB dye solutions undergoing photocatalytic degradation. To ensure effective separation, an isocratic elution method was employed, utilizing the H 2 O and ACN combination as the mobile phase. In Figures S1 a and b, the UPLC analysis results for the photocatalytic degradation of RhB dye in the presence of CZ-NC are presented. The graph shows a distinct decrease in RhB dye intensity over time, indicating significant degradation 62 .

LC-HRMS analysis and prediction of toxicity

To investigate the degradation mechanism and intermediates of RhB dye, LC-HRMS analysis was conducted (Figures S2 - S4 ). When the photocatalyst absorbs light, it generates radicals that initiate the degradation of RhB dye. The degradation process involves radical reactions such as N-de-ethylation, de-carboxylation, de-amination, de-alkylation, chromophore cleavage, and ring-opening, ultimately leading to mineralization. In Figure S5, the hydroxyl radicals are shown transforming RhB dye into N-de-ethylated RhB, with subsequent radical attacks causing decarboxylation in the molecular structure, followed by deamination. This sustained action results in the generation of fragments, yielding identified products such as 5-amino-2-(2-hydroxybenzyl) phenol (m/z = 215), 9H-xanthene-3,6-diamine (m/z = 212), 4-aminobenzene-1,2-diol (m/z = 125), glutaric acid (m/z = 132), and 4-aminobut-3-enoic acid (m/z = 101). These findings are in line with earlier reports in the literature 63 , 64 .

After identifying the intermediates resulting from the photocatalytic degradation of RhB dye, ECOSAR software was employed for toxicity analysis. Acute toxicity, associated with short-term exposure, was assessed using Lethal or Effect Concentration (LC 50 /EC 50 ) values, while Chronic toxicity, related to long-term exposure, was evaluated using Chronic values (ChV). Indicator species, including fish, Daphnid, and green algae, were utilized, and the ecotoxicity of both RhB dye and its photocatalytic degradation intermediates was estimated in mg/L, as summarized in Table S2. Certain degraded intermediates (S.No. 9–13) exhibited notably low (LC 50 /EC 50 , ChV) values, indicating toxicity to all indicator species. In contrast, the degraded products, such as hydroxylated and ring-opening structures (S. No. 15, 16, 19, 20), demonstrated higher values, suggesting lower harm compared to the parent molecule RhB. Importantly, with sufficient photocatalytic degradation time, these products could undergo detoxification by decomposing and transforming into CO 2 , H 2 O, and NO 3 - and NH 4 + ions.

Catalysts recyclability and stability

The catalysts were recycled from the reaction mixture separately, undergoing multiple cleaning cycles with water and ethanol before drying to assess stability and cost-effectiveness. Subsequently, the reused catalysts were employed in successive degradation reactions. In Fig.  13 a, the degradation efficiency of the catalyst remained consistent for up to four cycles. Figure  13 b shows the stability of the recycled catalyst evaluated using XRD. No significant changes were observed in the peak pattern when compared with freshly prepared CuO NPs, ZnO NPs, and CZ-NC. Thus, it demonstrated high stability, making it suitable for the photocatalytic degradation of RhB dye. A comparison between the present study and previously reported results on photocatalytic RhB dye degradation is presented in Table 2 . The results indicated that the prepared nanomaterials can be used as a better catalyst for the treatment of textile dye effluent.

( a ) Recyclability of CZ-NC, ( b ) XRD spectra of freshly prepared and recycled catalysts after the 4th cycle.

This study employed an environmentally friendly method to synthesize CuO NPs, ZnO NPs, and CZ-NC using aqueous leaf extract from TI. The plant-derived phytoconstituents acted as both reducing and capping agents, as confirmed by FT-IR spectroscopy. XRD patterns verified the formation of two-phase CuO and ZnO compounds. Band edge potential calculations for CuO and ZnO suggested significant photocatalytic efficiency in RhB dye degradation for the nanocomposite. CZ-NC displayed remarkable efficiency, achieving 96.1% degradation over 105 min, surpassing CuO NPs (78%) and ZnO NPs (83%) with a calculated reaction rate of 4.8333 × 10 –4  s -1 . The identified and analyzed products during photocatalysis indicated the generation of relatively harmless substances. The catalyst exhibited good recyclability, maintaining activity for up to 4 cycles, as confirmed by powder XRD analysis, showing no phase change even after the 4 th cycle. Consequently, this catalyst is effective in degrading harmful pollutants, demonstrating its potential application in addressing environmental challenges.

Data availability

Data is provided in the supplementary information.

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Acknowledgements

The study presented here was facilitated by the ''VIT SEED Grant-RGEMS Fund (SG20220085)'' from the Vellore Institute of Technology in Vellore, India.

This work was supported by Vellore Institute of Technology Vellore, India, under the ''VIT SEED Grant-RGEMS Fund (SG20220085)''.

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Jeevarathinam, M., Asharani, I.V. Synthesis of CuO, ZnO nanoparticles, and CuO-ZnO nanocomposite for enhanced photocatalytic degradation of Rhodamine B: a comparative study. Sci Rep 14 , 9718 (2024). https://doi.org/10.1038/s41598-024-60008-7

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Inorganic Chemistry Frontiers

A chelating coordination modulation method for the synthesis of ti-mof single crystals †.

* Corresponding authors

a State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian 350002, P. R. China E-mail: [email protected] , [email protected].

b University of Chinese Academy of Sciences, Beijing 100049, P. R. China

Even though titanium-based metal–organic frameworks (Ti-MOFs) are promising as efficient photocatalysts, the high reactivity of titanium ions makes the synthesis and structure determination of new Ti-MOFs quite challenging. In this study, we propose a chelating coordination modulation (CCM) method for the synthesis of Ti-MOF single crystals by using molecules with chelating coordination groups as the modulator. Thanks to this method, three Ti-MOFs (FIR-117–119) have been obtained and their structures were determined by single crystal X-ray diffraction (SCXRD), validating the universality of this approach. By capturing the intermediate and determining its single crystal structure, the role of the modulator in the growth of Ti-MOF single crystals is clarified: the use of a chelating coordination molecule as the modulator and a competitive ligand slows down the reaction rate by forming Ti–modulator key intermediates, which balance the formation of Ti-MOFs and growth of single crystals. Furthermore, FIR-119 exhibits excellent photocatalytic performance under visible light due to its good light absorption ability with a narrow bandgap. These results highlight the potential of the chelating coordination modulation method in the synthesis of new photoactive Ti-MOFs and their single crystals.

• This article is part of the themed collection: 2024 Inorganic Chemistry Frontiers HOT articles

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A chelating coordination modulation method for the synthesis of Ti-MOF single crystals

H. Li, S. Li, F. Wang and J. Zhang, Inorg. Chem. Front. , 2024, Advance Article , DOI: 10.1039/D4QI00436A

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Towards novel promising perovskite-type ferroelectric materials: High-pressure synthesis of rubidium niobate

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Capacitors are crucial components in electronic devices such as smartphones and computers. They are made of dielectric materials that polarize on the application of the voltage. Currently, barium titanate ((BaTiO₃) is the most widely used material for capacitors. Barium titanate belongs to the perovskite group of materials, where a titanium ion resides within an oxygen octahedral cage. The material exhibits displacive-type ferroelectric behavior, where the displacement of ions during the phase transition leads to the creation of a permanent dipole moment within the material.

In a study published in the journal Dalton Transactions , 2024, 53, 7044 -7052 on April 1, 2024, researchers led by Professor Ayako Yamamoto from the Shibaura Institute of Technology, including master student Kimitoshi Murase have developed a displacement-type ferroelectric material with a high dielectric constant. The theoretical part was investigated by Dr. Hiroki Moriwake and his group from the Japan Fine Ceramics Center.

Employing a high-pressure method, researchers successfully incorporated sizable rubidium ions into perovskite-type compounds, resulting in the synthesis of rubidium niobate (RbNbO 3 ). This compound, previously known for its challenging synthesis process, was effectively created through an innovative approach. RbNbO 3 exhibits displacement ferroelectricity like BaTiO 3 , making it a promising candidate for capacitors and interest in synthesizing RbNbO 3 dates back to the 1970s. However, investigations into its dielectric properties have only been conducted at low temperatures (below 27°C). This study sheds light on the crystal structure and phase transitions across a broad temperature range (-268 to +800°C), paving the way for further research and development.

"The high-pressure synthesis method has reported a variety of materials with perovskite-type structures, including superconductors and magnets. In this study, our focus was on combining niobates and alkali metals known for their high dielectric properties," says Prof. Yamamoto.

The researchers synthesized non-perovskite-type RbNbO 3 by sintering a mixture of rubidium carbonate and niobium oxide at 1073 K (800°C), then subjected it to high pressures of 40,000 atmospheres at 1173 K (900°C)for 30 minutes. Under these high-pressure and high-temperature conditions, the rubidium niobate underwent a structural transformation from a complex triclinic phase at ambient pressure phase into a 26 % denser orthorhombic perovskite-type structure.

Using X-ray diffraction, the researchers investigated the crystal structure. Their analysis using a single crystal revealed that the crystal structure closely resembled that of potassium niobate (KNbO 3 ) and exhibited similar distortions observed in BaTiO 3 , both well-known ferroelectric materials. However, they found that the orthorhombicity and displacement of niobium atoms in RbNbO 3 exceeded those of KNbO 3 , indicating a higher degree of dielectric polarization due to phase transitions.

Furthermore, through powder X-ray diffraction, researchers identified four distinct phase transitions occurring across temperatures ranging from -268°C to +800°C. Below room temperature, RbNbO 3 exists in an orthorhombic phase, which is the most stable configuration. As the temperature rises, it undergoes transitions: first to a tetragonal perovskite phase above 220°C, then into a more elongated tetragonal perovskite phase beyond 300°C. Finally, above 420°C, it reverts to a non-perovskite phase found under atmospheric conditions.

These observed phase transitions closely match predictions made through first-principles calculations. The researchers also calculated the dielectric polarization of different phases of RbNbO 3 . They found that the orthorhombic phase had a polarization of 0.33 C m −2 , while the two tetragonal phases showed polarizations of 0.4 and 0.6 C m −2 , respectively. These values are comparable to those of ferroelectric alkali metal niobates such as KNbO 3 (0.32 C m −2 ), LiNbO 3 (0.71 C m −2 ), and LiTaO 3 (0.50 C m −2 ).

"The high-pressure phase obtained this time confirmed the presence of a polar structure from the observation of second harmonic generation of the same strength as potassium niobate, and a relatively high relative permittivity was also obtained. As for the dielectric constant, it is expected that values equal to or greater than those of potassium niobate can be obtained by increasing the sample density, as predicted from theoretical calculations," says Prof. Yamamoto.

The researchers are planning further experiments to accurately measure the dielectric constant and demonstrate the high polarization of RbNbO 3 . The advantage of the high-pressure method lies in its ability to stabilize substances that do not exist under atmospheric pressure. Using the proposed method, larger alkali metal ions such as cesium could be incorporated into the perovskite structure, leading to ferroelectrics with desirable dielectric properties.

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Materials provided by Shibaura Institute of Technology . Note: Content may be edited for style and length.

Journal Reference :

• Ayako Yamamoto, Kimitoshi Murase, Takeru Sato, Kazumasa Sugiyama, Toru Kawamata, Yoshiyuki Inaguma, Jun-ichi Yamaura, Kazuki Shitara, Rie Yokoi, Hiroki Moriwake. Crystal structure and properties of perovskite-type rubidium niobate, a high-pressure phase of RbNbO3 . Dalton Transactions , 2024; 53 (16): 7044 DOI: 10.1039/d4dt00190g

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An overview of methodological approaches in systematic reviews

Prabhakar veginadu.

1 Department of Rural Clinical Sciences, La Trobe Rural Health School, La Trobe University, Bendigo Victoria, Australia

Hanny Calache

2 Lincoln International Institute for Rural Health, University of Lincoln, Brayford Pool, Lincoln UK

Akshaya Pandian

3 Department of Orthodontics, Saveetha Dental College, Chennai Tamil Nadu, India

Mohd Masood

Associated data.

APPENDIX B: List of excluded studies with detailed reasons for exclusion

APPENDIX C: Quality assessment of included reviews using AMSTAR 2

The aim of this overview is to identify and collate evidence from existing published systematic review (SR) articles evaluating various methodological approaches used at each stage of an SR.

The search was conducted in five electronic databases from inception to November 2020 and updated in February 2022: MEDLINE, Embase, Web of Science Core Collection, Cochrane Database of Systematic Reviews, and APA PsycINFO. Title and abstract screening were performed in two stages by one reviewer, supported by a second reviewer. Full‐text screening, data extraction, and quality appraisal were performed by two reviewers independently. The quality of the included SRs was assessed using the AMSTAR 2 checklist.

The search retrieved 41,556 unique citations, of which 9 SRs were deemed eligible for inclusion in final synthesis. Included SRs evaluated 24 unique methodological approaches used for defining the review scope and eligibility, literature search, screening, data extraction, and quality appraisal in the SR process. Limited evidence supports the following (a) searching multiple resources (electronic databases, handsearching, and reference lists) to identify relevant literature; (b) excluding non‐English, gray, and unpublished literature, and (c) use of text‐mining approaches during title and abstract screening.

The overview identified limited SR‐level evidence on various methodological approaches currently employed during five of the seven fundamental steps in the SR process, as well as some methodological modifications currently used in expedited SRs. Overall, findings of this overview highlight the dearth of published SRs focused on SR methodologies and this warrants future work in this area.

1. INTRODUCTION

Evidence synthesis is a prerequisite for knowledge translation. 1 A well conducted systematic review (SR), often in conjunction with meta‐analyses (MA) when appropriate, is considered the “gold standard” of methods for synthesizing evidence related to a topic of interest. 2 The central strength of an SR is the transparency of the methods used to systematically search, appraise, and synthesize the available evidence. 3 Several guidelines, developed by various organizations, are available for the conduct of an SR; 4 , 5 , 6 , 7 among these, Cochrane is considered a pioneer in developing rigorous and highly structured methodology for the conduct of SRs. 8 The guidelines developed by these organizations outline seven fundamental steps required in SR process: defining the scope of the review and eligibility criteria, literature searching and retrieval, selecting eligible studies, extracting relevant data, assessing risk of bias (RoB) in included studies, synthesizing results, and assessing certainty of evidence (CoE) and presenting findings. 4 , 5 , 6 , 7

The methodological rigor involved in an SR can require a significant amount of time and resource, which may not always be available. 9 As a result, there has been a proliferation of modifications made to the traditional SR process, such as refining, shortening, bypassing, or omitting one or more steps, 10 , 11 for example, limits on the number and type of databases searched, limits on publication date, language, and types of studies included, and limiting to one reviewer for screening and selection of studies, as opposed to two or more reviewers. 10 , 11 These methodological modifications are made to accommodate the needs of and resource constraints of the reviewers and stakeholders (e.g., organizations, policymakers, health care professionals, and other knowledge users). While such modifications are considered time and resource efficient, they may introduce bias in the review process reducing their usefulness. 5

Substantial research has been conducted examining various approaches used in the standardized SR methodology and their impact on the validity of SR results. There are a number of published reviews examining the approaches or modifications corresponding to single 12 , 13 or multiple steps 14 involved in an SR. However, there is yet to be a comprehensive summary of the SR‐level evidence for all the seven fundamental steps in an SR. Such a holistic evidence synthesis will provide an empirical basis to confirm the validity of current accepted practices in the conduct of SRs. Furthermore, sometimes there is a balance that needs to be achieved between the resource availability and the need to synthesize the evidence in the best way possible, given the constraints. This evidence base will also inform the choice of modifications to be made to the SR methods, as well as the potential impact of these modifications on the SR results. An overview is considered the choice of approach for summarizing existing evidence on a broad topic, directing the reader to evidence, or highlighting the gaps in evidence, where the evidence is derived exclusively from SRs. 15 Therefore, for this review, an overview approach was used to (a) identify and collate evidence from existing published SR articles evaluating various methodological approaches employed in each of the seven fundamental steps of an SR and (b) highlight both the gaps in the current research and the potential areas for future research on the methods employed in SRs.

An a priori protocol was developed for this overview but was not registered with the International Prospective Register of Systematic Reviews (PROSPERO), as the review was primarily methodological in nature and did not meet PROSPERO eligibility criteria for registration. The protocol is available from the corresponding author upon reasonable request. This overview was conducted based on the guidelines for the conduct of overviews as outlined in The Cochrane Handbook. 15 Reporting followed the Preferred Reporting Items for Systematic reviews and Meta‐analyses (PRISMA) statement. 3

2.1. Eligibility criteria

Only published SRs, with or without associated MA, were included in this overview. We adopted the defining characteristics of SRs from The Cochrane Handbook. 5 According to The Cochrane Handbook, a review was considered systematic if it satisfied the following criteria: (a) clearly states the objectives and eligibility criteria for study inclusion; (b) provides reproducible methodology; (c) includes a systematic search to identify all eligible studies; (d) reports assessment of validity of findings of included studies (e.g., RoB assessment of the included studies); (e) systematically presents all the characteristics or findings of the included studies. 5 Reviews that did not meet all of the above criteria were not considered a SR for this study and were excluded. MA‐only articles were included if it was mentioned that the MA was based on an SR.

SRs and/or MA of primary studies evaluating methodological approaches used in defining review scope and study eligibility, literature search, study selection, data extraction, RoB assessment, data synthesis, and CoE assessment and reporting were included. The methodological approaches examined in these SRs and/or MA can also be related to the substeps or elements of these steps; for example, applying limits on date or type of publication are the elements of literature search. Included SRs examined or compared various aspects of a method or methods, and the associated factors, including but not limited to: precision or effectiveness; accuracy or reliability; impact on the SR and/or MA results; reproducibility of an SR steps or bias occurred; time and/or resource efficiency. SRs assessing the methodological quality of SRs (e.g., adherence to reporting guidelines), evaluating techniques for building search strategies or the use of specific database filters (e.g., use of Boolean operators or search filters for randomized controlled trials), examining various tools used for RoB or CoE assessment (e.g., ROBINS vs. Cochrane RoB tool), or evaluating statistical techniques used in meta‐analyses were excluded. 14

2.2. Search

The search for published SRs was performed on the following scientific databases initially from inception to third week of November 2020 and updated in the last week of February 2022: MEDLINE (via Ovid), Embase (via Ovid), Web of Science Core Collection, Cochrane Database of Systematic Reviews, and American Psychological Association (APA) PsycINFO. Search was restricted to English language publications. Following the objectives of this study, study design filters within databases were used to restrict the search to SRs and MA, where available. The reference lists of included SRs were also searched for potentially relevant publications.

The search terms included keywords, truncations, and subject headings for the key concepts in the review question: SRs and/or MA, methods, and evaluation. Some of the terms were adopted from the search strategy used in a previous review by Robson et al., which reviewed primary studies on methodological approaches used in study selection, data extraction, and quality appraisal steps of SR process. 14 Individual search strategies were developed for respective databases by combining the search terms using appropriate proximity and Boolean operators, along with the related subject headings in order to identify SRs and/or MA. 16 , 17 A senior librarian was consulted in the design of the search terms and strategy. Appendix A presents the detailed search strategies for all five databases.

2.3. Study selection and data extraction

Title and abstract screening of references were performed in three steps. First, one reviewer (PV) screened all the titles and excluded obviously irrelevant citations, for example, articles on topics not related to SRs, non‐SR publications (such as randomized controlled trials, observational studies, scoping reviews, etc.). Next, from the remaining citations, a random sample of 200 titles and abstracts were screened against the predefined eligibility criteria by two reviewers (PV and MM), independently, in duplicate. Discrepancies were discussed and resolved by consensus. This step ensured that the responses of the two reviewers were calibrated for consistency in the application of the eligibility criteria in the screening process. Finally, all the remaining titles and abstracts were reviewed by a single “calibrated” reviewer (PV) to identify potential full‐text records. Full‐text screening was performed by at least two authors independently (PV screened all the records, and duplicate assessment was conducted by MM, HC, or MG), with discrepancies resolved via discussions or by consulting a third reviewer.

Data related to review characteristics, results, key findings, and conclusions were extracted by at least two reviewers independently (PV performed data extraction for all the reviews and duplicate extraction was performed by AP, HC, or MG).

2.4. Quality assessment of included reviews

The quality assessment of the included SRs was performed using the AMSTAR 2 (A MeaSurement Tool to Assess systematic Reviews). The tool consists of a 16‐item checklist addressing critical and noncritical domains. 18 For the purpose of this study, the domain related to MA was reclassified from critical to noncritical, as SRs with and without MA were included. The other six critical domains were used according to the tool guidelines. 18 Two reviewers (PV and AP) independently responded to each of the 16 items in the checklist with either “yes,” “partial yes,” or “no.” Based on the interpretations of the critical and noncritical domains, the overall quality of the review was rated as high, moderate, low, or critically low. 18 Disagreements were resolved through discussion or by consulting a third reviewer.

2.5. Data synthesis

To provide an understandable summary of existing evidence syntheses, characteristics of the methods evaluated in the included SRs were examined and key findings were categorized and presented based on the corresponding step in the SR process. The categories of key elements within each step were discussed and agreed by the authors. Results of the included reviews were tabulated and summarized descriptively, along with a discussion on any overlap in the primary studies. 15 No quantitative analyses of the data were performed.

From 41,556 unique citations identified through literature search, 50 full‐text records were reviewed, and nine systematic reviews 14 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 were deemed eligible for inclusion. The flow of studies through the screening process is presented in Figure  1 . A list of excluded studies with reasons can be found in Appendix B .

Study selection flowchart

3.1. Characteristics of included reviews

Table  1 summarizes the characteristics of included SRs. The majority of the included reviews (six of nine) were published after 2010. 14 , 22 , 23 , 24 , 25 , 26 Four of the nine included SRs were Cochrane reviews. 20 , 21 , 22 , 23 The number of databases searched in the reviews ranged from 2 to 14, 2 reviews searched gray literature sources, 24 , 25 and 7 reviews included a supplementary search strategy to identify relevant literature. 14 , 19 , 20 , 21 , 22 , 23 , 26 Three of the included SRs (all Cochrane reviews) included an integrated MA. 20 , 21 , 23

Characteristics of included studies

SR = systematic review; MA = meta‐analysis; RCT = randomized controlled trial; CCT = controlled clinical trial; N/R = not reported.

The included SRs evaluated 24 unique methodological approaches (26 in total) used across five steps in the SR process; 8 SRs evaluated 6 approaches, 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 while 1 review evaluated 18 approaches. 14 Exclusion of gray or unpublished literature 21 , 26 and blinding of reviewers for RoB assessment 14 , 23 were evaluated in two reviews each. Included SRs evaluated methods used in five different steps in the SR process, including methods used in defining the scope of review ( n  = 3), literature search ( n  = 3), study selection ( n  = 2), data extraction ( n  = 1), and RoB assessment ( n  = 2) (Table  2 ).

Summary of findings from review evaluating systematic review methods

There was some overlap in the primary studies evaluated in the included SRs on the same topics: Schmucker et al. 26 and Hopewell et al. 21 ( n  = 4), Hopewell et al. 20 and Crumley et al. 19 ( n  = 30), and Robson et al. 14 and Morissette et al. 23 ( n  = 4). There were no conflicting results between any of the identified SRs on the same topic.

3.2. Methodological quality of included reviews

Overall, the quality of the included reviews was assessed as moderate at best (Table  2 ). The most common critical weakness in the reviews was failure to provide justification for excluding individual studies (four reviews). Detailed quality assessment is provided in Appendix C .

3.3. Evidence on systematic review methods

3.3.1. methods for defining review scope and eligibility.

Two SRs investigated the effect of excluding data obtained from gray or unpublished sources on the pooled effect estimates of MA. 21 , 26 Hopewell et al. 21 reviewed five studies that compared the impact of gray literature on the results of a cohort of MA of RCTs in health care interventions. Gray literature was defined as information published in “print or electronic sources not controlled by commercial or academic publishers.” Findings showed an overall greater treatment effect for published trials than trials reported in gray literature. In a more recent review, Schmucker et al. 26 addressed similar objectives, by investigating gray and unpublished data in medicine. In addition to gray literature, defined similar to the previous review by Hopewell et al., the authors also evaluated unpublished data—defined as “supplemental unpublished data related to published trials, data obtained from the Food and Drug Administration  or other regulatory websites or postmarketing analyses hidden from the public.” The review found that in majority of the MA, excluding gray literature had little or no effect on the pooled effect estimates. The evidence was limited to conclude if the data from gray and unpublished literature had an impact on the conclusions of MA. 26

Morrison et al. 24 examined five studies measuring the effect of excluding non‐English language RCTs on the summary treatment effects of SR‐based MA in various fields of conventional medicine. Although none of the included studies reported major difference in the treatment effect estimates between English only and non‐English inclusive MA, the review found inconsistent evidence regarding the methodological and reporting quality of English and non‐English trials. 24 As such, there might be a risk of introducing “language bias” when excluding non‐English language RCTs. The authors also noted that the numbers of non‐English trials vary across medical specialties, as does the impact of these trials on MA results. Based on these findings, Morrison et al. 24 conclude that literature searches must include non‐English studies when resources and time are available to minimize the risk of introducing “language bias.”

3.3.2. Methods for searching studies

Crumley et al. 19 analyzed recall (also referred to as “sensitivity” by some researchers; defined as “percentage of relevant studies identified by the search”) and precision (defined as “percentage of studies identified by the search that were relevant”) when searching a single resource to identify randomized controlled trials and controlled clinical trials, as opposed to searching multiple resources. The studies included in their review frequently compared a MEDLINE only search with the search involving a combination of other resources. The review found low median recall estimates (median values between 24% and 92%) and very low median precisions (median values between 0% and 49%) for most of the electronic databases when searched singularly. 19 A between‐database comparison, based on the type of search strategy used, showed better recall and precision for complex and Cochrane Highly Sensitive search strategies (CHSSS). In conclusion, the authors emphasize that literature searches for trials in SRs must include multiple sources. 19

In an SR comparing handsearching and electronic database searching, Hopewell et al. 20 found that handsearching retrieved more relevant RCTs (retrieval rate of 92%−100%) than searching in a single electronic database (retrieval rates of 67% for PsycINFO/PsycLIT, 55% for MEDLINE, and 49% for Embase). The retrieval rates varied depending on the quality of handsearching, type of electronic search strategy used (e.g., simple, complex or CHSSS), and type of trial reports searched (e.g., full reports, conference abstracts, etc.). The authors concluded that handsearching was particularly important in identifying full trials published in nonindexed journals and in languages other than English, as well as those published as abstracts and letters. 20

The effectiveness of checking reference lists to retrieve additional relevant studies for an SR was investigated by Horsley et al. 22 The review reported that checking reference lists yielded 2.5%–40% more studies depending on the quality and comprehensiveness of the electronic search used. The authors conclude that there is some evidence, although from poor quality studies, to support use of checking reference lists to supplement database searching. 22

3.3.3. Methods for selecting studies

Three approaches relevant to reviewer characteristics, including number, experience, and blinding of reviewers involved in the screening process were highlighted in an SR by Robson et al. 14 Based on the retrieved evidence, the authors recommended that two independent, experienced, and unblinded reviewers be involved in study selection. 14 A modified approach has also been suggested by the review authors, where one reviewer screens and the other reviewer verifies the list of excluded studies, when the resources are limited. It should be noted however this suggestion is likely based on the authors’ opinion, as there was no evidence related to this from the studies included in the review.

Robson et al. 14 also reported two methods describing the use of technology for screening studies: use of Google Translate for translating languages (for example, German language articles to English) to facilitate screening was considered a viable method, while using two computer monitors for screening did not increase the screening efficiency in SR. Title‐first screening was found to be more efficient than simultaneous screening of titles and abstracts, although the gain in time with the former method was lesser than the latter. Therefore, considering that the search results are routinely exported as titles and abstracts, Robson et al. 14 recommend screening titles and abstracts simultaneously. However, the authors note that these conclusions were based on very limited number (in most instances one study per method) of low‐quality studies. 14

3.3.4. Methods for data extraction

Robson et al. 14 examined three approaches for data extraction relevant to reviewer characteristics, including number, experience, and blinding of reviewers (similar to the study selection step). Although based on limited evidence from a small number of studies, the authors recommended use of two experienced and unblinded reviewers for data extraction. The experience of the reviewers was suggested to be especially important when extracting continuous outcomes (or quantitative) data. However, when the resources are limited, data extraction by one reviewer and a verification of the outcomes data by a second reviewer was recommended.

As for the methods involving use of technology, Robson et al. 14 identified limited evidence on the use of two monitors to improve the data extraction efficiency and computer‐assisted programs for graphical data extraction. However, use of Google Translate for data extraction in non‐English articles was not considered to be viable. 14 In the same review, Robson et al. 14 identified evidence supporting contacting authors for obtaining additional relevant data.

3.3.5. Methods for RoB assessment

Two SRs examined the impact of blinding of reviewers for RoB assessments. 14 , 23 Morissette et al. 23 investigated the mean differences between the blinded and unblinded RoB assessment scores and found inconsistent differences among the included studies providing no definitive conclusions. Similar conclusions were drawn in a more recent review by Robson et al., 14 which included four studies on reviewer blinding for RoB assessment that completely overlapped with Morissette et al. 23

Use of experienced reviewers and provision of additional guidance for RoB assessment were examined by Robson et al. 14 The review concluded that providing intensive training and guidance on assessing studies reporting insufficient data to the reviewers improves RoB assessments. 14 Obtaining additional data related to quality assessment by contacting study authors was also found to help the RoB assessments, although based on limited evidence. When assessing the qualitative or mixed method reviews, Robson et al. 14 recommends the use of a structured RoB tool as opposed to an unstructured tool. No SRs were identified on data synthesis and CoE assessment and reporting steps.

4. DISCUSSION

4.1. summary of findings.

Nine SRs examining 24 unique methods used across five steps in the SR process were identified in this overview. The collective evidence supports some current traditional and modified SR practices, while challenging other approaches. However, the quality of the included reviews was assessed to be moderate at best and in the majority of the included SRs, evidence related to the evaluated methods was obtained from very limited numbers of primary studies. As such, the interpretations from these SRs should be made cautiously.

The evidence gathered from the included SRs corroborate a few current SR approaches. 5 For example, it is important to search multiple resources for identifying relevant trials (RCTs and/or CCTs). The resources must include a combination of electronic database searching, handsearching, and reference lists of retrieved articles. 5 However, no SRs have been identified that evaluated the impact of the number of electronic databases searched. A recent study by Halladay et al. 27 found that articles on therapeutic intervention, retrieved by searching databases other than PubMed (including Embase), contributed only a small amount of information to the MA and also had a minimal impact on the MA results. The authors concluded that when the resources are limited and when large number of studies are expected to be retrieved for the SR or MA, PubMed‐only search can yield reliable results. 27

Findings from the included SRs also reiterate some methodological modifications currently employed to “expedite” the SR process. 10 , 11 For example, excluding non‐English language trials and gray/unpublished trials from MA have been shown to have minimal or no impact on the results of MA. 24 , 26 However, the efficiency of these SR methods, in terms of time and the resources used, have not been evaluated in the included SRs. 24 , 26 Of the SRs included, only two have focused on the aspect of efficiency 14 , 25 ; O'Mara‐Eves et al. 25 report some evidence to support the use of text‐mining approaches for title and abstract screening in order to increase the rate of screening. Moreover, only one included SR 14 considered primary studies that evaluated reliability (inter‐ or intra‐reviewer consistency) and accuracy (validity when compared against a “gold standard” method) of the SR methods. This can be attributed to the limited number of primary studies that evaluated these outcomes when evaluating the SR methods. 14 Lack of outcome measures related to reliability, accuracy, and efficiency precludes making definitive recommendations on the use of these methods/modifications. Future research studies must focus on these outcomes.

Some evaluated methods may be relevant to multiple steps; for example, exclusions based on publication status (gray/unpublished literature) and language of publication (non‐English language studies) can be outlined in the a priori eligibility criteria or can be incorporated as search limits in the search strategy. SRs included in this overview focused on the effect of study exclusions on pooled treatment effect estimates or MA conclusions. Excluding studies from the search results, after conducting a comprehensive search, based on different eligibility criteria may yield different results when compared to the results obtained when limiting the search itself. 28 Further studies are required to examine this aspect.

Although we acknowledge the lack of standardized quality assessment tools for methodological study designs, we adhered to the Cochrane criteria for identifying SRs in this overview. This was done to ensure consistency in the quality of the included evidence. As a result, we excluded three reviews that did not provide any form of discussion on the quality of the included studies. The methods investigated in these reviews concern supplementary search, 29 data extraction, 12 and screening. 13 However, methods reported in two of these three reviews, by Mathes et al. 12 and Waffenschmidt et al., 13 have also been examined in the SR by Robson et al., 14 which was included in this overview; in most instances (with the exception of one study included in Mathes et al. 12 and Waffenschmidt et al. 13 each), the studies examined in these excluded reviews overlapped with those in the SR by Robson et al. 14

One of the key gaps in the knowledge observed in this overview was the dearth of SRs on the methods used in the data synthesis component of SR. Narrative and quantitative syntheses are the two most commonly used approaches for synthesizing data in evidence synthesis. 5 There are some published studies on the proposed indications and implications of these two approaches. 30 , 31 These studies found that both data synthesis methods produced comparable results and have their own advantages, suggesting that the choice of the method must be based on the purpose of the review. 31 With increasing number of “expedited” SR approaches (so called “rapid reviews”) avoiding MA, 10 , 11 further research studies are warranted in this area to determine the impact of the type of data synthesis on the results of the SR.

4.2. Implications for future research

The findings of this overview highlight several areas of paucity in primary research and evidence synthesis on SR methods. First, no SRs were identified on methods used in two important components of the SR process, including data synthesis and CoE and reporting. As for the included SRs, a limited number of evaluation studies have been identified for several methods. This indicates that further research is required to corroborate many of the methods recommended in current SR guidelines. 4 , 5 , 6 , 7 Second, some SRs evaluated the impact of methods on the results of quantitative synthesis and MA conclusions. Future research studies must also focus on the interpretations of SR results. 28 , 32 Finally, most of the included SRs were conducted on specific topics related to the field of health care, limiting the generalizability of the findings to other areas. It is important that future research studies evaluating evidence syntheses broaden the objectives and include studies on different topics within the field of health care.

4.3. Strengths and limitations

To our knowledge, this is the first overview summarizing current evidence from SRs and MA on different methodological approaches used in several fundamental steps in SR conduct. The overview methodology followed well established guidelines and strict criteria defined for the inclusion of SRs.

There are several limitations related to the nature of the included reviews. Evidence for most of the methods investigated in the included reviews was derived from a limited number of primary studies. Also, the majority of the included SRs may be considered outdated as they were published (or last updated) more than 5 years ago 33 ; only three of the nine SRs have been published in the last 5 years. 14 , 25 , 26 Therefore, important and recent evidence related to these topics may not have been included. Substantial numbers of included SRs were conducted in the field of health, which may limit the generalizability of the findings. Some method evaluations in the included SRs focused on quantitative analyses components and MA conclusions only. As such, the applicability of these findings to SR more broadly is still unclear. 28 Considering the methodological nature of our overview, limiting the inclusion of SRs according to the Cochrane criteria might have resulted in missing some relevant evidence from those reviews without a quality assessment component. 12 , 13 , 29 Although the included SRs performed some form of quality appraisal of the included studies, most of them did not use a standardized RoB tool, which may impact the confidence in their conclusions. Due to the type of outcome measures used for the method evaluations in the primary studies and the included SRs, some of the identified methods have not been validated against a reference standard.

Some limitations in the overview process must be noted. While our literature search was exhaustive covering five bibliographic databases and supplementary search of reference lists, no gray sources or other evidence resources were searched. Also, the search was primarily conducted in health databases, which might have resulted in missing SRs published in other fields. Moreover, only English language SRs were included for feasibility. As the literature search retrieved large number of citations (i.e., 41,556), the title and abstract screening was performed by a single reviewer, calibrated for consistency in the screening process by another reviewer, owing to time and resource limitations. These might have potentially resulted in some errors when retrieving and selecting relevant SRs. The SR methods were grouped based on key elements of each recommended SR step, as agreed by the authors. This categorization pertains to the identified set of methods and should be considered subjective.

5. CONCLUSIONS

This overview identified limited SR‐level evidence on various methodological approaches currently employed during five of the seven fundamental steps in the SR process. Limited evidence was also identified on some methodological modifications currently used to expedite the SR process. Overall, findings highlight the dearth of SRs on SR methodologies, warranting further work to confirm several current recommendations on conventional and expedited SR processes.

CONFLICT OF INTEREST

The authors declare no conflicts of interest.

Supporting information

APPENDIX A: Detailed search strategies

ACKNOWLEDGMENTS

The first author is supported by a La Trobe University Full Fee Research Scholarship and a Graduate Research Scholarship.

Open Access Funding provided by La Trobe University.

Veginadu P, Calache H, Gussy M, Pandian A, Masood M. An overview of methodological approaches in systematic reviews . J Evid Based Med . 2022; 15 :39–54. 10.1111/jebm.12468 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]