CINeMA: Software for semiautomated assessment of the confidence in the results of network meta‐analysis
Open accessAbstract
Network meta‐analysis (NMA) compares several interventions that are linked in a network of comparative studies and estimates the relative treatment effects between all treatments, using both direct and indirect evidence. NMA is increasingly used for decision making in health care, however, a user‐friendly system to evaluate the confidence that can be placed in the results of NMA is currently lacking. This paper is a tutorial describing the Confidence In Network Meta‐Analysis (CINeMA) web application, which is based on the framework developed by Salanti et al (2014, PLOS One , 9, e99682) and refined by Nikolakopoulou et al (2019, bioRxiv ). Six domains that affect the level of confidence in the NMA results are considered: (a) within‐study bias, (b) reporting bias, (c) indirectness, (d) imprecision, (e) heterogeneity, and (f) incoherence. CINeMA is freely available and open‐source and no login is required. In the configuration step users upload their data, produce network plots and define the analysis and effect measure. The dataset should include assessments of study‐level risk of bias and judgments on indirectness. CINeMA calls the netmeta routine in R to estimate relative effects and heterogeneity. Users are then guided through a systematic evaluation of the six domains. In this way reviewers assess the level of concerns for each relative treatment effect from NMA as giving rise to “no concerns,” “some concerns,” or “major concerns” in each of the six domains, which are graphically summarized on the report page for all effect estimates. Finally, judgments across the domains are summarized into a single confidence rating (“high,” “moderate,” “low,” or “very low”). In conclusion, the user‐friendly web‐based CINeMA platform provides a transparent framework to evaluate evidence from systematic reviews with multiple interventions.
AI snapshot
Written from the paper’s full text.
TL;DR
CINeMA is a freely available, open-source web application that semiautomates assessment of confidence in network meta-analysis results using six domains: within-study bias, reporting bias, indirectness, imprecision, heterogeneity, and incoherence. It guides users from data upload and NMA configuration through domain judgments to a final confidence rating of high, moderate, low, or very low.
Key findings
- CINeMA implements the Salanti et al. (2014) framework refined by Nikolakopoulou et al. (2019), with optional rules for automated domain judgments that users can partially or fully override.
- In the worked example, the uploaded network included 22 randomized trials, 6 interventions, and 14 comparisons with direct data; the NMA found diabetes risk was lower with ARB and higher with diuretics than with placebo, with odds ratios of 0.827 (95% CI 0.691–0.990) for ARB versus placebo and 1.330 (95% CI 1.124–1.573) for diuretics versus placebo.
- For within-study bias in ACE versus beta blockers, low-RoB studies contributed 53%, moderate-RoB studies 43%, and high-RoB studies 4%; the Majority, Average, and Highest rules yielded “No concerns,” “Some concerns,” and “Major concerns,” respectively.
- For imprecision, with a clinically important odds ratio of 1.2, the diuretics-versus-placebo confidence interval of 1.12–1.57 was judged “No concerns”; for heterogeneity, beta blockers versus placebo had a confidence interval of 1.05–1.46 and prediction interval of 0.90–1.70, yielding “Some concerns,” with estimated between-study variance τ² = 0.016.
Methods
- Design: tutorial describing the CINeMA web application, its data formats, default rules, and rationale, based on the CINeMA framework.
- Data/sample: worked example using a network meta-analysis of antihypertensive drugs and diabetes incidence, including 22 randomized trials comparing ACE inhibitors, ARBs, calcium-channel blockers, beta blockers, diuretics, and placebo.
- Analysis: CINeMA calls the netmeta routine in R to estimate relative effects and heterogeneity, calculates a contribution matrix, and applies semiautomated rules across six domains; judgments are summarized on a report page into an overall confidence rating.
Limitations
- The paper states that subjectivity is inevitable in evidence evaluation, including interpretation of heterogeneity and definition of a clinically important effect size, and that a study examining CINeMA’s reproducibility would be of interest.
- The paper states that CINeMA currently does not support multiple outcomes per project, multiple concurrent projects, or assessment of dependency between outcomes; time-to-event and rate data are also not supported, and the reporting bias domain is planned for further development.
- The text makes evident that the worked example’s indirectness data are fictional, reporting bias judgments are illustrative, and the clinically important odds ratio of 1.2 is not justified by clinical or epidemiological reasoning.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
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Cites
Statistics
Claims the paper makes
0 of 27 cited sources checked · 0 supported · 0 flagged
- Findingnone given
CINeMA considers the per-study contribution matrix in conjunction with RoB assessments to evaluate each relative treatment effect with respect to within-study bias.
“CINeMA considers the per‐study contribution matrix in conjunction with RoB assessments to evaluate each relative treatment effect with respect to within‐study bias.”
Quote in the paperSubstantial claim- Rests on an assumption: High risk of bias should be associated with “Major concerns,” moderate risk of bias with “Some concerns,” and low risk of bias with “No concerns.”
- Findingreasoning
Three rules can be used to summarize the risk of within-study bias and of indirectness for each relative effect estimate and produce automated judgments.
“Three rules can be used to summarize the risk of within‐study bias and of indirectness for each relative effect estimate and produce automated judgments.”
Quote in the paperSubstantial claim - Premisecitation
CINeMA is based on the framework developed by Salanti et al. (2014) and refined by Nikolakopoulou et al. (2019).
“This paper is a tutorial describing the Confidence In Network Meta‐Analysis (CINeMA) web application, which is based on the framework developed by Salanti et al (2014, PLOS One, 9, e99682) and refined by Nikolakopoulou et al (2019, bioRxiv).”
- Also stated in: § INTRODUCTION · plain
“To evaluate the confidence in the results of NMA, a framework has been developed (Salanti, Del Giovane, Chaimani, Caldwell, & Higgins, 2014) and recently refined (Nikolakopoulou et al., 2019), which is called CINeMA (Confidence In Network Meta‐Analysis).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Salanti et al., 2014
Not checked in the free run.
- cites Nikolakopoulou et al., 2019
Not checked in the free run.
- Also stated in: § INTRODUCTION · plain
- Premisecitation
The CINeMA framework considers six domains that affect the level of confidence in NMA results: within-study bias, reporting bias, indirectness, imprecision, heterogeneity, and incoherence.
“Six domains that affect the level of confidence in the NMA results are considered: (a) within‐study bias, (b) reporting bias, (c) indirectness, (d) imprecision, (e) heterogeneity, and (f) incoherence.”
- Also stated in: § INTRODUCTION · plain
“The CINeMA framework considers six domains that affect the level of confidence in the NMA results: (a) within‐study bias, (b) reporting bias, (c) indirectness, (d) imprecision, (e) heterogeneity, and (f) incoherence.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Salanti et al., 2014
Not checked in the free run.
- cites Nikolakopoulou et al., 2019
Not checked in the free run.
- Also stated in: § INTRODUCTION · plain
- Interpretationreasoning
The user-friendly web-based CINeMA platform provides a transparent framework to evaluate evidence from systematic reviews with multiple interventions.
“In conclusion, the user‐friendly web‐based CINeMA platform provides a transparent framework to evaluate evidence from systematic reviews with multiple interventions.”
Quote in the paperSubstantial claim- Rests on an assumption: A user-friendly system to evaluate the confidence that can be placed in the results of NMA is currently lacking.
- Premisecitation
The methodology described in Nikolakopoulou et al. (2019) has been implemented in CINeMA using "rules" that can automate derivation of domain-specific judgments.
“The methodology described in (Nikolakopoulou et al., 2019) has been implemented in CINeMA using “rules” that can automate derivation of domain‐specific judgments.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Nikolakopoulou et al., 2019
Not checked in the free run.
- Premisecitation
The network included 22 randomized trials which evaluated the differences between ACE, ARB, CCB, Beta Blocker, diuretics, and placebo.
“The network included 22 randomized trials which evaluated the differences between angiotensin‐converting‐enzyme inhibitors (ACE), angiotensin‐receptor blockers (ARB), calcium‐channel blocker (CCB), Beta Blocker, diuretics, and placebo.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Elliott and Meyer, 2007
Not checked in the free run.
- Premisereasoning
CINeMA's approach for indirectness intends to also address the assumption of transitivity by indicating which comparisons may suffer from different definitions of the setting of interest.
“CINeMA's approach for indirectness intends to also address the assumption of transitivity by indicating which comparisons may suffer from different definitions of the setting of interest.”
Quote in the paperSubstantial claim- The authors state this as an assumption: A core assumption in NMA is that of transitivity; that there is an underlying true relative treatment effect which applies to all studies regardless of the treatments being compared.
- Classified as probably not a substantial claim. 27
- May combine two claims. 9
Probably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenCINeMA does not permanently store the data or any other information related to uploaded projects; only temporary storage takes place.
“CINeMA does not permanently store the data, or any other information related to the uploaded projects; only temporary storage takes place for the sake of the calculations or network efficiency.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingnone givenCINeMA is freely available and open-source and no login is required.
“CINeMA is freely available and open‐source and no login is required.”
- Also stated in: § DISCUSSION · plain
“CINeMA is freely available and open‐source and no login is required.”
Quote in the paperNot a substantial claim- Also stated in: § DISCUSSION · plain
Probably not a substantial claim: may not pass S3 (load-bearing).
FindingcitationThe CINeMA framework has been implemented in a user-friendly web application.
“The CINeMA framework has been implemented in a user‐friendly web application (see https://cinema.ispm.unibe.ch/; CINeMA, 2017).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataIn the worked example, CINeMA recognizes the file format (long) and outcome type (binary) and provides a summary of the dataset: 22 studies, 6 interventions, and 14 comparisons with direct data.
“Uploading the network of antihypertensive drugs, CINeMA recognizes the file format (long) and outcome type (binary) and provides summary of the dataset: it includes 22 studies, 6 interventions, and 14 comparisons with direct data.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Findingnone givenThe rules for judging imprecision and heterogeneity are based on whether the confidence interval or prediction interval includes the line of no-effect and prespecified clinically important treatment effects.
“The rules for judging imprecision and heterogeneity are based on whether the confidence interval or prediction interval includes the line of no‐effect and prespecified clinically important treatment effects.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataIn the worked example, studies at low RoB contribute 53% in the estimation of ACE versus Beta Blockers, 43% of the contribution comes from studies at moderate RoB, and studies at high RoB contribute the remaining 4%.
“Studies at low RoB contribute 53% in the estimation of ACE versus Beta Blockers, 43% of the contribution comes from studies at moderate RoB, and studies at high RoB contribute the remaining 4%.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
FindingreasoningIn the worked example, these RoB contributions resolve into "No concerns," "Some concerns," and "Major concerns" using the "Majority RoB," "Average RoB," and "Highest RoB" rules respectively.
“These RoB contributions resolve into “No concerns,” “Some concerns,” and “Major concerns” using the “Majority RoB,” “Average RoB,” and “Highest RoB” rules respectively.”
Quote in the paperNot a substantial claim- Rests on an assumption: High risk of bias should be associated with “Major concerns,” moderate risk of bias with “Some concerns,” and low risk of bias with “No concerns.”
Probably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenCurrently, other types of outcomes, for example, time to event or rate data, are not supported by CINeMA.
“Currently, other types of outcomes, for example, time to event or rate data, are not supported.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
FindingreasoningCINeMA software can be used to facilitate evaluation of results of NMA.
“We have outlined how CINeMA software can be used to facilitate evaluation of results of NMA.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
PremisecitationReviewers assess the level of concerns for each relative treatment effect from NMA as giving rise to "no concerns," "some concerns," or "major concerns" in each of the six domains.
“Reviewers assess the level of concerns for each relative treatment effect from NMA as giving rise to “no concerns,” “some concerns,” or “major concerns” in each of the six domains.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Salanti et al., 2014
Not checked in the free run.
- cites Nikolakopoulou et al., 2019
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Classified as probably not a substantial claim.
PremisecitationNetwork meta-analysis (NMA) is increasingly used for decision making in health care.
“Network meta‐analysis (NMA) is increasingly being used to make decisions about optimal interventions in health care (Kanters et al., 2016; Petropoulou et al., 2016).”
- Also stated in: § Abstract · plain
“NMA is increasingly used for decision making in health care”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Kanters et al., 2016
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- cites Petropoulou et al., 2016
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- Also stated in: § Abstract · plain
Classified as probably not a substantial claim.
PremisecitationJudgments across the six domains are summarized into a single confidence rating: high, moderate, low, or very low.
“Finally, judgments across the domains are summarized into a single confidence rating (“high,” “moderate,” “low,” or “very low”).”
- Also stated in: § INTRODUCTION · plain
“Then, judgments across the domains are summarized into a single confidence rating (“high,” “moderate,” “low,” or “very low”).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Salanti et al., 2014
Not checked in the free run.
- cites Nikolakopoulou et al., 2019
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- Also stated in: § INTRODUCTION · plain
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationThe NMA found that the risk of diabetes was lower with ARB, and higher with diuretics than with placebo.
“The NMA found that the risk of diabetes was lower with ARB, and higher with diuretics than with placebo (Elliott and Meyer, 2007).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Elliott and Meyer, 2007
Not checked in the free run.
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Interpretationnone givenCINeMA, with semiautomation of methods via a guided on-line process, greatly simplifies evaluation of NMA results, particularly for large networks.
“CINeMA, with semiautomation of methods via a guided on‐line process greatly simplifies this process, particularly for large networks.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
PremisecitationPrediction intervals provide a range within which the true effect of a new study is likely to lie.
“Prediction intervals provide a range within which the true effect of a new study is likely to lie (Riley, Higgins, & Deeks, 2011).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Riley, Higgins, & Deeks, 2011
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Probably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenImprecision and Heterogeneity are related as big heterogeneity will also affect the precision of relative treatment effects.
““Imprecision” and “Heterogeneity” are also related as big heterogeneity will also affect the precision of relative treatment effects.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationStudy-level RoB would normally summarize considerations on selection, performance, attrition, detection, and reporting bias.
“Study‐level RoB would normally summarize considerations on selection, performance, attrition, detection, and reporting bias (Higgins et al., 2011), while study‐level indirectness refers to deviations between the data and the targeted research question.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Higgins et al., 2011
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Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
InterpretationcitationWhile the main guiding principles of the CINeMA framework have been established, specific methods, recommendations, and implementation of automated rules in CINeMA software are evolving.
“While the main guiding principles of the CINeMA framework have been established (Nikolakopoulou et al., 2019; Salanti et al., 2014), specific methods, recommendations, and implementation of automated rules in CINeMA software are evolving.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Nikolakopoulou et al., 2019
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- cites Salanti et al., 2014
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PremisecitationThe design-by-treatment interaction test is a global method to assess incoherence.
“the first is a global method to assess incoherence, the design‐by‐treatment interaction test (Higgins et al., 2012; White et al., 2012).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Higgins et al., 2012
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- cites White et al., 2012
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Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.
InterpretationreasoningThe clearly defined criteria on which judgments are based should increase reproducibility and the fact that CINeMA is open-source ensures transparency.
“The clearly defined criteria on which judgments are based should increase reproducibility and the fact that CINeMA is open‐source ensures transparency.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Premisenone givenAssuming that transitivity holds implies that consistency-which refers to the agreement of the estimated treatment effects-also holds.
“Assuming that transitivity holds implies that consistency—which refers to the agreement of the estimated treatment effects—also holds.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationEarly applications of CINeMA have appeared in the literature.
“Early applications of CINeMA have appeared in the literature (Cipriani et al., 2018; Schwingshackl et al., 2018).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Cipriani et al., 2018
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- cites Schwingshackl et al., 2018
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May combine two claims.
PremisecitationReference quantiles are taken from empirical studies and are specific to the type of outcome and comparison.
“The reference quantiles are taken from empirical studies and are specific to the type of outcome and comparison (Rhodes, Turner, & Higgins, 2015; Turner, Davey, Clarke, Thompson, & Higgins, 2012).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Rhodes, Turner, & Higgins, 2015
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- cites Turner, Davey, Clarke, Thompson, & Higgins, 2012
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PremisecitationThe contribution matrix is calculated using the flow decomposition method described in Papakonstantinou et al. (2018) and is used later in the evaluation of within-study bias and indirectness.
“It is calculated using the flow decomposition method described in (Papakonstantinou et al., 2018) and is used later in the evaluation of within‐study bias and indirectness.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Papakonstantinou et al., 2018
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PremisecitationCINeMA is largely based on the methodological framework described previously.
“It is largely based on the methodological framework described previously (Nikolakopoulou et al., 2019; Salanti et al., 2014).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Nikolakopoulou et al., 2019
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- cites Salanti et al., 2014
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Interpretationnone givenIndirectness and Incoherence domains are closely related, as they both refer to considerations of similarity across included studies which could or could not manifest statistically in the data.
“For example, “Indirectness” and “Incoherence” domains are closely related, as they both refer to considerations of similarity across included studies which could or could not manifest statistically in the data.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationSIDE is a method to separate indirect from direct evidence.
“the results of the second method, Separating Indirect from Direct Evidence (SIDE; Dias et al., 2010), are shown”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Dias et al., 2010
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InterpretationreasoningCINeMA is a transparent framework to evaluate evidence from systematic reviews with multiple interventions.
“CINeMA is a transparent framework to evaluate evidence from systematic reviews with multiple interventions”
Quote in the paperSubstantial claim
The paper relies on this without support.
A user-friendly system to evaluate the confidence that can be placed in the results of NMA is currently lacking.
“a user‐friendly system to evaluate the confidence that can be placed in the results of NMA is currently lacking.”
The paper relies on this without support.
High risk of bias should be associated with “Major concerns,” moderate risk of bias with “Some concerns,” and low risk of bias with “No concerns.”
“High RoB should be associated with “Major concerns,” moderate RoB with “Some concerns,” and low RoB with “No concerns.””
The authors state this as an assumption.
A core assumption in NMA is that of transitivity; that there is an underlying true relative treatment effect which applies to all studies regardless of the treatments being compared.
“A core assumption in NMA is that of transitivity; that there is an underlying true relative treatment effect which applies to all studies regardless of the treatments being compared.”
The authors state this as an assumption.
Treatment effects approximately follow the normal distribution.
“assumes that treatment effects approximately follow the normal distribution.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
CINeMA was used to assess the credibility of the findings of each network meta-analysis.
“Finally, to assess the credibility of the findings of each network meta-analysis, the findings were incorporated into the Confidence in Network Meta-Analysis (CINeMA) application, which is an adaptation of the Grading of Recommendations Assessment, Development, and Evaluation approach [19–21].”
From Antidepressants for the treatment of adults with major depressive disorder in the maintenance phase: a systematic review and network meta-analysis (2023) · cites this paper as [21]
The CINeMA tool was employed to assess the quality of evidence in six domains.
“The Confidence in Network Meta-Analysis (CINeMA) tool [30, 31] was employed to assess the quality of evidence in six domains, including within-study bias, across-studies bias, indirectness, imprecision, heterogeneity, and incoherence.”
From Comparing caloric restriction regimens for effective weight management in adults: a systematic review and network meta-analysis (2024) · cites this paper as [31]
The certainty of evidence was evaluated using the CINeMA framework.
“The certainty of evidence was evaluated using the CINeMA (Confidence in Network Meta-Analysis) framework, which adapts the GRADE approach for network meta-analyses, addressing within-study bias, reporting bias, indirectness, imprecision, heterogeneity, and inconsistency”
Confidence in effect estimates for the primary outcomes was evaluated using the CINeMA framework.
“We evaluated Confidence In Network Meta-Analysis (CINeMA) effect estimates for the primary outcomes utilising the CINeMA methodological framework and application [17, 25].”
Heterogeneity for the primary outcomes was evaluated based on the agreement between CIs and prediction intervals.
“We evaluated heterogeneity for the primary outcomes based on the agreement between CIs and prediction intervals in relation to the null effect and the clinically important effect on the opposite direction to the point estimate [16, 17].”
Certainty of treatment effect estimates was assessed using the CINeMA approach covering six domains of bias.
“Certainty of treatment effect estimates for each outcome at the treatment level were assessed using the Confidence in Network Meta-Analysis (CINeMA) approach, covering six domains of bias: within study bias, across study bias, indirectness, imprecision, heterogeneity, and inconsistency.50 51”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Heterogeneity rules point to the imprecision panel
MinorThe heterogeneity section says its rules are illustrated in Figure 4a, but the figure caption assigns heterogeneity to panel 4b and imprecision to panel 4a.
“Our judgments based on a hypothetical, clinically important odds ratio of 0.8 are summarized and illustrated in Table 4 and in Figure 4a.”
“Figure 4 Illustration of rules to assess imprecision (a), heterogeneity (b), and incoherence (c) in CINeMA.”
Contribution-matrix bars are described as columns rather than rows
MinorThe paper says each bar represents a relative-effect estimate, while its description of the contribution matrix places relative effects in rows and studies in columns. A bar for one relative effect therefore corresponds to a row, not a column.
“Each bar corresponds to an estimate of relative effect. Each bar also represents a reordering of a column of the per‐study contribution matrix”
“The contribution matrix shows the percentage contribution of information from each study and each direct comparison (shown in columns) to the estimation of each relative effect (shown in rows).”
Cites
The Cochrane Collaboration's tool for assessing risk of bias in randomised trials
Higgins, J. P. T., Altman, D. G., Gotzsche, P. C., Juni, P., Moher, D., Oxman, A. D., Savovic, J., Schulz, K. F., Weeks, L., Sterne, J. A. C., et al. (2011) · BMJ · doi:10.1136/bmj.d5928 · Cited by 33,817 · see citing works
Self-citationDermyshi, E., Wang, Y., Yan, C., Hong, W., Qiu, G., Gong, X., & Zhang, T. (2017) · Neonatology · doi:10.1159/000454668 · Cited by 24,577 · see citing works
Cipriani, A., Furukawa, T. A., Salanti, G., Chaimani, A., Atkinson, L. Z., Ogawa, Y., Leucht, S., Ruhe, H. G., Turner, E. H., Higgins, J. P. T., et al. (2018) · The Lancet · doi:10.1016/s0140-6736(17)32802-7 · Cited by 3,541 · cited 2× · see citing works
“Early applications of CINeMA have appeared in the literature (Cipriani et al., 2018; Schwingshackl et al., 2018).”
Self-citationInterpretation of random effects meta-analyses
Riley, R. D., Higgins, J. P. T., & Deeks, J. J. (2011) · BMJ · doi:10.1136/bmj.d549 · Cited by 2,884 · see citing works
Self-citationChecking consistency in mixed treatment comparison meta-analysis
Dias, S., Welton, N. J., Caldwell, D. M., & Ades, A. E. (2010) · Statistics in Medicine · doi:10.1002/sim.3767 · Cited by 2,521 · see citing works
Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies
Higgins, J. P. T., Jackson, D., Barrett, J. K., Lu, G., Ades, A. E., & White, I. R. (2012) · Research Synthesis Methods · doi:10.1002/jrsm.1044 · Cited by 2,098 · see citing works
Self-citationCINeMA: an approach for assessing confidence in the results of a network meta-analysis
Nikolakopoulou, A., Higgins, J. P. T., Papakonstantinou, T., Chaimani, A., Del Giovane, C., Egger, M., & Salanti, G. (2020) · PLOS Medicine · doi:10.1371/journal.pmed.1003082 · Cited by 1,935 · see citing works
Self-citationEvaluating the quality of evidence from a network meta-analysis
Salanti, G., Del Giovane, C., Chaimani, A., Caldwell, D. M., & Higgins, J. P. T. (2014) · PLoS ONE · doi:10.1371/journal.pone.0099682 · Cited by 1,402 · see citing works
Self-citationWhite, I. R., Barrett, J. K., Jackson, D., & Higgins, J. P. T. (2012) · Research Synthesis Methods · doi:10.1002/jrsm.1045 · Cited by 1,114 · see citing works
Self-citationIncident diabetes in clinical trials of antihypertensive drugs: a network meta-analysis
Elliott, W. J., & Meyer, P. M. (2007) · The Lancet · doi:10.1016/s0140-6736(07)60108-1 · Cited by 1,016 · see citing works
Turner, R. M., Davey, J., Clarke, M. J., Thompson, S. G., & Higgins, J. P. (2012) · International Journal of Epidemiology · doi:10.1093/ije/dys041 · Cited by 837 · see citing works
Self-citationPublication bias: evidence of delayed publication in a cohort study of clinical research projects
Stern, J. M., & Simes, R. J. (1997) · BMJ · doi:10.1136/bmj.315.7109.640 · Cited by 818 · see citing works
Evidence Synthesis for Decision Making 4
Dias, S., Welton, N. J., Sutton, A. J., Caldwell, D. M., Lu, G., & Ades, A. E. (2013) · Medical Decision Making · doi:10.1177/0272989x12455847 · Cited by 734 · see citing works
Chaimani, A., & Salanti, G. (2012) · Research Synthesis Methods · doi:10.1002/jrsm.57 · Cited by 521 · cited 1× · see citing works
“” Completeness of the search, considerations related to the particular field (based on existing evidence and empirical studies), and statistical methods undertaken should inform the assessment of reporting bias for each relative treatment effect (Chaimani and Salanti, 2012; Mavridis, Sutton, Cipriani, & Salanti, 2013;…”
Self-citationRhodes, K. M., Turner, R., & Higgins, J. P. T. (2014) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2014.08.012 · Cited by 398 · see citing works
Self-citationFunctional and therapeutic potential of inulin: A comprehensive review
Ahmed, W., & Rashid, S. (2017) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2017.1355775 · Cited by 338 · see citing works
Schwingshackl, L., Chaimani, A., Schwedhelm, C., Toledo, E., Pünsch, M., HOFFMANN, G. E., & Boeing, H. (2018) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2018.1463967 · Cited by 165 · see citing works
Πετροπούλου, Μ., Nikolakopoulou, A., Veroniki, A. A., Rios, P. D. L., Vafaei, A., Zarin, W., Gianatsi, M., Sullivan, S. M., Tricco, A. C., Chaimani, A., et al. (2016) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2016.11.002 · Cited by 135 · see citing works
Self-citationUse of network meta-analysis in clinical guidelines
Kanters, S., Ford, N., Druyts, E., Thorlund, K., Mills, E. J., & Bansback, N. (2016) · Bulletin of the World Health Organization · doi:10.2471/blt.16.174326 · Cited by 121 · see citing works
Dickersin, K., & Chalmers, I. (2011) · Journal of the Royal Society of Medicine · doi:10.1258/jrsm.2011.11k042 · Cited by 108 · see citing works
Network Meta-Analysis using Frequentist Methods
Rücker, G., Schwarzer, G., Krahn, U., & König, J. (2015) · Cited by 106 · see citing works
Mavridis, D., Sutton, A. J., Cipriani, A., & Salanti, G. (2012) · Statistics in Medicine · doi:10.1002/sim.5494 · Cited by 83 · see citing works
Self-citationA selection model for accounting for publication bias in a full network meta‐analysis
Mavridis, D., Welton, N. J., Sutton, A. J., & Salanti, G. (2014) · Statistics in Medicine · doi:10.1002/sim.6321 · Cited by 75 · see citing works
Self-citationAssessing Confidence in the Results of Network Meta-Analysis (Cinema)Preprint
Nikolakopoulou, A., Higgins, J. P. T., Papakonstantinou, T., Chaimani, A., Giovane, C. D., Egger, M., & Salanti, G. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/597047 · Cited by 49 · see citing works
Self-citationEstimating the contribution of studies in network meta-analysis: paths, flows and streams
Papakonstantinou, T., Nikolakopoulou, A., Rücker, G., Chaimani, A., Schwarzer, G., Egger, M., & Salanti, G. (2018) · F1000Research · doi:10.12688/f1000research.14770.1 · Cited by 49 · see citing works
Self-citationEstimating the contribution of studies in network meta-analysis: paths, flows and streams
Papakonstantinou, T., Nikolakopoulou, A., Rücker, G., Chaimani, A., Schwarzer, G., Egger, M., & Salanti, G. (2018) · F1000Research · doi:10.12688/f1000research.14770.3 · Cited by 37 · see citing works
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CINeMA: an approach for assessing confidence in the results of a network meta-analysis
Nikolakopoulou, A., Higgins, J. P. T., Papakonstantinou, T., Chaimani, A., Del Giovane, C., Egger, M., & Salanti, G. (2020) · PLOS Medicine · doi:10.1371/journal.pmed.1003082 · Cited by 1,935 · see citing works
Self-citationNoetel, M., Sanders, T., Gallardo-Gómez, D., Taylor, P., del Pozo Cruz, B., van den Hoek, D., Smith, J. J., Mahoney, J., Spathis, J., Moresi, M., et al. (2024) · BMJ · doi:10.1136/bmj-2023-075847 · Cited by 707 · see citing works
Yao, H., Zhang, A., Li, D., Wu, Y., Wang, C.-Z., Wan, J.-Y., & Yuan, C.-S. (2024) · BMJ · doi:10.1136/bmj-2023-076410 · Cited by 558 · see citing works
Gavelin, H. M., Dong, C., Minkov, R., Bahar-Fuchs, A., Ellis, K. A., Lautenschlager, N. T., Mellow, M. L., Wade, A. T., Smith, A. E., Finke, C., et al. (2021) · Ageing Research Reviews · doi:10.1016/j.arr.2020.101232 · Cited by 371 · see citing works
Bighelli, I., Rodolico, A., García‐Mieres, H., Pitschel‐Walz, G., Hansen, W., Schneider‐Thoma, J., Siafis, S., Wu, H., Wang, D., Salanti, G., et al. (2021) · The Lancet Psychiatry · doi:10.1016/s2215-0366(21)00243-1 · Cited by 291 · see citing works
Self-citationBarberio, B., Gracie, D. J., Black, C. J., & Ford, A. C. (2022) · Gut · doi:10.1136/gutjnl-2022-328052 · Cited by 189 · see citing works
Efficacy and Safety of 8 Atropine Concentrations for Myopia Control in Children
Ha, A., Kim, S.-J., Shim, S. R., Kim, Y. K., & Jung, J. H. (2021) · Ophthalmology · doi:10.1016/j.ophtha.2021.10.016 · Cited by 189 · see citing works
Kishi, T., Ikuta, T., Matsuda, Y., Sakuma, K., Okuya, M., Nomura, I., Hatano, M., & Iwata, N. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01334-4 · Cited by 163 · see citing works
McIsaac, D. I., Kidd, G., Gillis, C., Branje, K., Al-Bayati, M., Baxi, A., Grudzinski, A. L., Boland, L., Veroniki, A.-A., Wolfe, D., et al. (2025) · BMJ · doi:10.1136/bmj-2024-081164 · Cited by 168 · see citing works
Rodolico, A., Bighelli, I., Avanzato, C., Concerto, C., Cutrufelli, P., Mineo, L., Schneider-Thoma, J., Siafis, S., Signorelli, M. S., Wu, H., et al. (2022) · The Lancet Psychiatry · doi:10.1016/s2215-0366(21)00437-5 · Cited by 157 · see citing works
Furukawa, T. A., Shinohara, K., Sahker, E., Karyotaki, E., Miguel, C., Ciharova, M., Bockting, C. L. H., Breedvelt, J. J. F., Tajika, A., Imai, H., et al. (2021) · World Psychiatry · doi:10.1002/wps.20906 · Cited by 126 · see citing works
Kishi, T., Ikuta, T., Sakuma, K., Okuya, M., Hatano, M., Matsuda, Y., & Iwata, N. (2023) · Molecular Psychiatry · doi:10.1038/s41380-022-01824-z · Cited by 136 · see citing works
Siafis, S., Çıray, O., Wu, H., Schneider‐Thoma, J., Bighelli, I., Krause, M., Rodolico, A., Ceraso, A., Deste, G., Huhn, M., et al. (2022) · Molecular Autism · doi:10.1186/s13229-022-00488-4 · Cited by 138 · see citing works
Terao, I., & Kodama, W. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102203 · Cited by 134 · see citing works
Yue, J.-L., Chang, X.-W., Zheng, J.-W., Shi, L., Xiang, Y., Que, J., Yuan, K., Teng, T., Teng, T., Li, Y., et al. (2023) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2023.101746 · Cited by 112 · see citing works
Fujii, T., Salanti, G., Belletti, A., Bellomo, R., Carr, A. C., Furukawa, T. A., Luethi, N., Luo, Y., Putzu, A., Sartini, C., et al. (2021) · Intensive Care Medicine · doi:10.1007/s00134-021-06558-0 · Cited by 112 · see citing works
Self-citationde Souza, M. H. G., Al‐Sharif, L., Antunes, V., Huang, D. Q., & Loomba, R. (2025) · Hepatology · doi:10.1097/hep.0000000000001254 · Cited by 113 · see citing works
Karagiannis, T., Malandris, K., Avgerinos, I., Stamati, A., Kakotrichi, P., Liakos, A., Vasilakou, D., Kakaletsis, N., Tsapas, A., & Bekiari, E. (2024) · Diabetologia · doi:10.1007/s00125-024-06144-1 · Cited by 102 · see citing works
Korang, S. K., von Rohden, E., Veroniki, A. A., Ong, G., Ngalamika, O., Siddiqui, F., Juul, S., Nielsen, E. E., Feinberg, J. B., Petersen, J. J., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0260733 · Cited by 96 · see citing works
Gupta, A., Venkataraman, M., Talukder, M., & Bamimore, M. A. (2022) · JAMA Dermatology · doi:10.1001/jamadermatol.2021.5743 · Cited by 96 · see citing works
Seidler, A. L., Libesman, S., Hunter, K. E., Barba, A., Aberoumand, M., Williams, J. G., Shrestha, N., Aagerup, J., Sotiropoulos, J. X., Montgomery, A. A., et al. (2023) · The Lancet · doi:10.1016/s0140-6736(23)02469-8 · Cited by 97 · see citing works
Majzoub, A. M., Nayfeh, T., Barnard, A., Munaganuru, N., Dave, S., Singh, S., Murad, M. H., & Loomba, R. (2021) · Alimentary Pharmacology & Therapeutics · doi:10.1111/apt.16583 · Cited by 92 · see citing works
Hodkinson, A., Tsimpida, D., Kontopantelis, E., Rutter, M. K., Mamas, M. A., & Panagioti, M. (2022) · BMJ · doi:10.1136/bmj-2021-067731 · Cited by 97 · see citing works
The efficacy of powered toothbrushes: A systematic review and network meta‐analysis
Thomassen, T. M. J. A., van der Weijden, G. A., & Slot, D. E. (2021) · International Journal of Dental Hygiene · doi:10.1111/idh.12563 · Cited by 81 · see citing works
Cheng, H., McGuinness, L. A., Elbers, R. G., MacArthur, G. J., Taylor, A., McAleenan, A., Dawson, S., López-López, J. A., Higgins, J. P. T., Cowlishaw, S., et al. (2020) · BMJ · doi:10.1136/bmj.m3934 · Cited by 78 · see citing works
Self-citationMedicinal herbs for the treatment of anxiety: a systematic review and network meta-analysis
Zhang, W., Yan, Y., Wu, Y., Yang, H., Zhu, P., Yan, F., Zhao, R., Tian, P., Wang, T., Fan, Q., et al. (2022) · Pharmacological Research · doi:10.1016/j.phrs.2022.106204 · Cited by 75 · see citing works
Theta burst stimulation for depression: a systematic review and network and pairwise meta-analysis
Kishi, T., Ikuta, T., Sakuma, K., Hatano, M., Matsuda, Y., Wilkening, J., Goya-Maldonado, R., Tik, M., Williams, N. R., Kito, S., et al. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02630-5 · Cited by 74 · see citing works
de Wit, A. E., de Vries, Y. A., de Boer, M. K., Scheper, C., Fokkema, A., Janssen, C. A. H., Giltay, E. J., & Schoevers, R. A. (2021) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2021.06.090 · Cited by 72 · see citing works
Doleman, B., Mathiesen, O., Sutton, A. J., Cooper, N. J., Lund, J. N., & Williams, J. P. (2023) · British Journal of Anaesthesia · doi:10.1016/j.bja.2023.02.041 · Cited by 71 · see citing works
Dong, S., Schneider-Thoma, J., Bighelli, I., Siafis, S., Wang, D., Burschinski, A., Schestag, K., Samara, M., & Leucht, S. (2024) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s00406-023-01654-2 · Cited by 68 · see citing works
Semnani‐Azad, Z., Khan, T. A., Chiavaroli, L., Chen, V., Bhatt, H. A., Chen, A., Chiang, N., Oguntala, J. O., Kabisch, S., Lau, D. C. W., et al. (2025) · BMJ · doi:10.1136/bmj-2024-082007 · Cited by 79 · see citing works
Minimally invasive treatments for benign prostatic hyperplasia: a Cochrane network meta‐analysis
Franco, J. V. A., Jung, J. H., Imamura, M., Borofsky, M. S., Omar, M. I., Liquitay, C. M. E., Young, S. J., Golzarian, J., Veroniki, A. A., Garegnani, L., et al. (2021) · British Journal of Urology · doi:10.1111/bju.15653 · Cited by 69 · see citing works
Zhang, S., Ren, S.-J., Zhu, D., Liu, T., Wang, L. Y., Zhao, J., Fan, X., & Gong, H. (2024) · BMC Medical Education · doi:10.1186/s12909-024-06291-4 · Cited by 63 · see citing works
Wewege, M. A., Bagg, M. K., Jones, M. D., Ferraro, M. C., Cashin, A. G., Rizzo, R. R. N., Leake, H. B., Hagstrom, A. D., Sharma, S., McLachlan, A. J., et al. (2023) · BMJ · doi:10.1136/bmj-2022-072962 · Cited by 66 · see citing works
Self-citationGoodoory, V. C., Khasawneh, M., Thakur, E. R., Everitt, H., Gudleski, G. D., Lackner, J. M., Moss-Morris, R. E., Simrén, M., Vasant, D. H., Moayyedi, P., et al. (2024) · Gastroenterology · doi:10.1053/j.gastro.2024.05.010 · Cited by 66 · see citing works
Pillinger, T., Arumuham, A., McCutcheon, R. A., D’Ambrosio, E., Basdanis, G., Branco, M., Carr, R., Finelli, V., Furukawa, T. A., Gee, S., et al. (2025) · The Lancet · doi:10.1016/s0140-6736(25)01293-0 · Cited by 64 · see citing works
Self-citationZhang, R., Noronha, J. C., Khan, T. A., McGlynn, N. D., Back, S., Grant, S., Kendall, C. W. C., & Sievenpiper, J. L. (2023) · Nutrients · doi:10.3390/nu15041050 · Cited by 57 · see citing works
Tong, E., Chen, Y., Ren, Y., Zhou, Y., Di, C., Zhou, Y., Shao, S., Qiu, S., Hong, Y., Yang, L., et al. (2022) · Frontiers in Nutrition · doi:10.3389/fnut.2022.951676 · Cited by 57 · see citing works
Efficacy and Safety of Drugs for Gastroparesis: Systematic Review and Network Meta-analysis
Ingrosso, M. R., Camilleri, M., Tack, J., Ianiro, G., Black, C. J., & Ford, A. C. (2023) · Gastroenterology · doi:10.1053/j.gastro.2022.12.014 · Cited by 53 · see citing works
Salahuddin, N. H., Schütz, A., Pitschel‐Walz, G., Mayer, S. F., Chaimani, A., Siafis, S., Priller, J., Leucht, S., & Bighelli, I. (2024) · The Lancet Psychiatry · doi:10.1016/s2215-0366(24)00136-6 · Cited by 48 · see citing works
Stuart, B., Maund, E., Wilcox, C., Sridharan, K., Sivaramakrishnan, G., Regas, C., Newell, D., Soulsby, I., Tang, K. F., Finlay, A. Y., et al. (2021) · British Journal of Dermatology · doi:10.1111/bjd.20080 · Cited by 48 · see citing works
Chierici, A. P., Chevalier, N., & Iannelli, A. (2022) · International Journal of Surgery · doi:10.1016/j.ijsu.2022.106677 · Cited by 43 · see citing works
Eck, R. J., Elling, T., Sutton, A. J., Wetterslev, J., Gluud, C. N., van der Horst, I. C. C., Gans, R. O. B., Meijer, K., & Keus, F. (2022) · BMJ · doi:10.1136/bmj-2022-070022 · Cited by 40 · see citing works
Xia, J., Yu, J., Xu, H., Zhou, Y., Li, H., Yin, S., Xu, D., Wang, Y., Xia, H., Liao, W., et al. (2023) · Pharmacological Research · doi:10.1016/j.phrs.2023.106647 · Cited by 44 · see citing works
Korang, S. K., Juul, S., Nielsen, E. E., Feinberg, J. B., Siddiqui, F., Ong, G., Klingenberg, S. L., Veroniki, A. A., Bu, F.-L., Thabane, L., et al. (2020) · Systematic Reviews · doi:10.1186/s13643-020-01516-1 · Cited by 41 · see citing works
Yadgarov, M. Y., Landoni, G., Бондоевич, Б. Л., Polyakov, P. A., Kadantseva, K., Smirnova, A. V., Kuznetsov, I. V., Shemetova, M. M., Yakovlev, A. А., & Likhvantsev, V. V. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1491358 · Cited by 41 · see citing works
Zhou, Z., Chen, C., Teo, E.-C., Zhang, Y., Huang, J., Xu, Y., & Gu, Y. (2022) · Antioxidants · doi:10.3390/antiox11091751 · Cited by 49 · see citing works
Kishi, T., Ikuta, T., Citrome, L., Sakuma, K., Hatano, M., Hamanaka, S., Nishii, Y., & Iwata, N. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03439-8 · Cited by 42 · see citing works
Boonmanunt, S. W., Pattanaprateep, O., Ongphiphadhanakul, B., McKay, G. J., Attia, J., Vlaev, I., & Thakkinstian, A. (2022) · Annals of Behavioral Medicine · doi:10.1093/abm/kaac066 · Cited by 41 · see citing works
Kamimura, Y., Yamamoto, N., Shiroshita, A., Miura, T., Tsuji, T., Someko, H., Imai, E., Kimura, R., & Sobue, K. (2023) · British Journal of Anaesthesia · doi:10.1016/j.bja.2023.09.006 · Cited by 39 · see citing works
Application of network meta-analysis in the field of physical activity and health promotion
Su, X., McDonough, D. J., Chu, H., Quan, M., & Gao, Z. (2020) · Journal of Sport and Health Science · doi:10.1016/j.jshs.2020.07.011 · Cited by 38 · see citing works
Hayashi, M., Yamamoto, N., Kuroda, N., Kano, K., Miura, T., Kamimura, Y., & Shiroshita, A. (2024) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2024.01.024 · Cited by 44 · see citing works
de Wit, A. E., de Vries, Y. A., de Boer, M. K., Scheper, C., Fokkema, A., Schoevers, R. A., & Giltay, E. J. (2021) · BJPsych Open · doi:10.1192/bjo.2021.64 · Cited by 38 · see citing works
Comparative Efficacy of Bronchiolitis Interventions in Acute Care: A Network Meta-analysis
Elliott, S. A., Gaudet, L., Fernandes, R. M., Vandermeer, B., Freedman, S. B., Johnson, D. W., Plint, A. C., Klassen, T. P., Allain, D., & Hartling, L. (2021) · PEDIATRICS · doi:10.1542/peds.2020-040816 · Cited by 37 · see citing works
Jo, Y., Park, S., Oh, C., Pak, Y., Jeong, K., Yun, S., Noh, C., Chung, W., Kim, Y. H., Ko, Y., et al. (2021) · Korean Journal of Anesthesiology · doi:10.4097/kja.21330 · Cited by 37 · see citing works
Rapid reviews methods series: Guidance on assessing the certainty of evidence
Gartlehner, G., Nußbaumer-Streit, B., Devane, D., Kahwati, L. C., Viswanathan, M., King, V. J., Qaseem, A., Akl, E. A., & Schuenemann, H. J. (2023) · BMJ Evidence-Based Medicine · doi:10.1136/bmjebm-2022-112111 · Cited by 37 · see citing works
Li, X., Hao, X., Liu, J., & Huang, J. (2024) · BMJ Evidence-Based Medicine · doi:10.1136/bmjebm-2023-112434 · Cited by 36 · see citing works
Saeteaw, M., Sanguanboonyaphong, P., Yoodee, J., Craft, K., Sawangjit, R., Ngamphaiboon, N., Shantavasinkul, P. C., Subongkot, S., & Chaiyakunapruk, N. (2020) · BMJ Supportive & Palliative Care · doi:10.1136/bmjspcare-2020-002601 · Cited by 36 · see citing works
Shahid, A., Thirumaran, A. J., Christensen, R., Venkatesha, V., Henriksen, M., Bowden, J. L., & Hunter, D. J. (2025) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2024.08.012 · Cited by 34 · see citing works
Huang, Z., Li, J., Liu, Y., & Zhou, Y. (2023) · Frontiers in Physiology · doi:10.3389/fphys.2023.1193223 · Cited by 39 · see citing works
Smedslund, G., Kjeken, I., Musial, F., Sexton, J., & Østerås, N. (2022) · Osteoarthritis and Cartilage Open · doi:10.1016/j.ocarto.2022.100242 · Cited by 38 · see citing works
Tao, R., Yang, Y., Wilson, M., Chang, J. R., Liu, C., & Sit, C. H. P. (2025) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-024-01702-7 · Cited by 38 · see citing works
Chen, Y., Li, W., Lu, C., Gao, X., Song, H., Zhang, Y., Zhao, S., Cai, G., Guo, Q., Zhou, D., et al. (2024) · EClinicalMedicine · doi:10.1016/j.eclinm.2024.102513 · Cited by 31 · see citing works
deSouza, I. S., Thode, H. C., Shrestha, P., Allen, R. W., Koos, J. A., & Singer, A. J. (2021) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2021.11.011 · Cited by 30 · see citing works
Toval, Á., Bakker, E. A., Granada-Maia, J. B., de Arenas‐Arroyo, S. N., Solis‐Urra, P., Eijsvogels, T. M. H., Esteban‐Cornejo, I., Martínez‐Vizcaíno, V., & Ortega, F. B. (2025) · European Heart Journal · doi:10.1093/eurheartj/ehae870 · Cited by 35 · see citing works
Garcia, S. P., Cureau, F. V., Iorra, F. D. Q., Bottino, L. G., Monteiro, L. E. R. C., Leivas, G., Umpierre, D., & Schaan, B. D. (2025) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2025.112027 · Cited by 39 · see citing works
Feliciano-Alfonso, J. E., Muñoz-Ortiz, J., Marín-Noriega, M. A., Vargas-Villanueva, A., Triviño-Blanco, L., Carvajal-Saiz, N., & de‐la‐Torre, A. (2021) · Systematic Reviews · doi:10.1186/s13643-021-01758-7 · Cited by 29 · see citing works
Markar, S. R., Andreou, A., Bonavina, L., Flórez, I. D., Huo, B., Kontouli, K., Low, D. E., Mavridis, D., Maynard, N., Moss, A., et al. (2022) · United European Gastroenterology Journal · doi:10.1002/ueg2.12318 · Cited by 28 · see citing works
Wu, Y., Shen, S.-F., Lee, S., Chen, L., & Tung, H. (2023) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2023.104949 · Cited by 30 · see citing works
Zhou, Y., Zhao, D., Zhu, X., Liu, L., Meng, M., Shao, X., Zhu, X., Xiang, J., He, J., Zhao, Y., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-023-02604-1 · Cited by 35 · see citing works
Bednárik, D. S., Földvári-Nagy, K. C., Simon, V., Rancz, A., Gede, N., Veres, D. S., Paraskevopoulos, P., Schnabel, T., Erőss, B. M., Hegyi, P., et al. (2025) · The Lancet Regional Health - Europe · doi:10.1016/j.lanepe.2024.101151 · Cited by 30 · see citing works
Werner, S. S., Binder, N., Toews, I., Schünemann, H. J., Meerpohl, J. J., & Schwingshackl, L. (2021) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2021.02.010 · Cited by 29 · see citing works
Hernández, E. E. L., Bonilla, J. P. H., Ugarte, D. H., García, M. M., González, J. C. M., Vázquez, L. A. Z., Moreno, E. O., Herevia, A. E. J., Fonseca, R. K. C., & Guerrero, P. R. (2023) · Langenbeck s Archives of Surgery · doi:10.1007/s00423-023-02954-w · Cited by 27 · see citing works
Srisurapanont, M., Suttajit, S., Kosachunhanun, N., Likhitsathian, S., Suradom, C., & Maneeton, B. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104731 · Cited by 27 · see citing works
Liang, S., Mijatovic, J., Li, A., Koemel, N. A., Nasir, R., Toniutti, C., Bell-Anderson, K. S., Skilton, M. R., & O’Leary, F. (2022) · Nutrients · doi:10.3390/nu15010076 · Cited by 28 · see citing works
Chiocchia, V., Holloway, A., & Salanti, G. (2023) · BMC Medical Research Methodology · doi:10.1186/s12874-023-02038-9 · Cited by 26 · see citing works
Self-citationLi, J., Fan, Y., Luo, R., Yin, N., Wang, Y., Jing, J., & Zhang, J. (2024) · Intensive and Critical Care Nursing · doi:10.1016/j.iccn.2024.103925 · Cited by 25 · see citing works
Preventing persistent postsurgical pain: A systematic review and component network meta‐analysis
Allen, C., Walker, A. M., Premji, Z., Beauchemin‐Turcotte, M., Wong, J., Soh, S., Hawboldt, G. S., Shinkaruk, K., & Archer, D. P. (2022) · European Journal of Pain · doi:10.1002/ejp.1915 · Cited by 25 · see citing works
Yoosuf, B. T., Panda, A. K., Muhammed Favas, K. T., Bharti, S. K., Devana, S. K., & Bansal, D. (2024) · Scientific Reports · doi:10.1038/s41598-024-61977-5 · Cited by 25 · see citing works
Romadlon, D. S., Tu, Y., Chen, Y., Hasan, F., Kurniawan, R., & Chiu, H. (2024) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.3840 · Cited by 29 · see citing works
Theta Burst Stimulation Protocols for Schizophrenia
Kishi, T., Ikuta, T., Sakuma, K., Hamanaka, S., Nishii, Y., Hatano, M., Kito, S., & Iwata, N. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.41159 · Cited by 24 · see citing works
Chen, H., Zhang, J., Li, S., Zhang, H., & Wei, L. (2025) · Journal of Clinical Nursing · doi:10.1111/jocn.17582 · Cited by 24 · see citing works
Daly, C., Mbuagbaw, L. C. E., Thabane, L., Straus, S. E., & Hamid, J. S. (2020) · BMC Medical Research Methodology · doi:10.1186/s12874-020-01128-2 · Cited by 24 · see citing works
Charitakis, E., Metelli, S., Karlsson, L., Antoniadis, A. P., Rizas, K. D., Liuba, I., Almroth, H., Jönsson, A. H., Schwieler, J. H., Tsartsalis, D., et al. (2022) · BMC Medicine · doi:10.1186/s12916-022-02385-2 · Cited by 23 · see citing works
Interventions for Glaucoma Medication Adherence Improvement
Ha, A., Jang, M., Shim, S. R., Kim, C. Y., Chang, I. B., & Kim, Y. K. (2022) · Ophthalmology · doi:10.1016/j.ophtha.2022.06.025 · Cited by 25 · see citing works
Miura, T., Takahashi, T., Watanabe, J., Kataoka, Y., Ae, R., Saito, H., Takeshita, K., & Miyakoshi, N. (2023) · BMC Musculoskeletal Disorders · doi:10.1186/s12891-023-06448-0 · Cited by 23 · see citing works
Ortiz-Seller, A., Martorell, P., Barranco, H., Pascual-Camps, I., Morcillo, E. J., & Ortiz, J. L. (2024) · Survey of Ophthalmology · doi:10.1016/j.survophthal.2024.02.005 · Cited by 27 · see citing works
Huang, J., Li, Y., Chen, M., Cai, Z., Cai, Z., & Jiang, Z. (2024) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-024-01657-9 · Cited by 25 · see citing works
“The Confidence in Network Meta-Analysis (CIN-eMA) tool [30, 31] was employed to assess the quality of evidence in six domains, including within-study bias, across-studies bias, indirectness, imprecision, heterogeneity, and incoherence.”
MethodSrisurapanont, M., Suradom, C., Suttajit, S., Kongsaengdao, S., & Maneeton, B. (2024) · General Hospital Psychiatry · doi:10.1016/j.genhosppsych.2024.02.008 · Cited by 22 · see citing works
Liu, Y., Niu, P., Yan, J., Ji, H., Wang, Z., Jin, X., Lv, L., Feng, C.-N., Du, X., Yang, F., et al. (2024) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2024.118297 · Cited by 22 · see citing works
Torres, T. R., Barcelos, A., Filipe, P., & Fonseca, J. E. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2020.618163 · Cited by 22 · see citing works
Bastounis, A., Langley, T. E., Davis, S., Paskins, Z., Gittoes, N. J. L., Leonardi‐Bee, J., & Sahota, O. (2022) · JBMR Plus · doi:10.1002/jbm4.10620 · Cited by 22 · see citing works
Wang, B., Chiu, H., Luh, H., Lin, C., Hsieh, S.-H., Chen, T., Wu, C.-R., & Chen, P. (2022) · PLoS ONE · doi:10.1371/journal.pone.0265932 · Cited by 21 · see citing works
Wu, S., Wang, L., He, Y., Shi, F., Zhuang, H., Mei, L., & Qian, Y.-L. (2023) · Complementary Therapies in Clinical Practice · doi:10.1016/j.ctcp.2023.101802 · Cited by 22 · see citing works
Barry, G. S., Sehmbi, H., Retter, S., Bailey, J. G., Tablante, R., & Uppal, V. (2023) · British Journal of Anaesthesia · doi:10.1016/j.bja.2023.08.024 · Cited by 22 · see citing works
Wang, X., Kong, L., Zhao, Z., Shi, Z., Chen, H., Lang, Y., Lin, X., Du, Q., & Zhou, H. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.953993 · Cited by 21 · see citing works
Komora, P., Vámos, O., Gede, N., Hegyi, P., Kelemen, K., Galvács, A., Varga, G., Kerémi, B., & Vág, J. (2024) · Scientific Reports · doi:10.1038/s41598-024-69367-7 · Cited by 26 · see citing works
Chan, B. S., Yu, D. S. F., Wong, C. W. Y., & Li, P. W. C. (2025) · European Journal of Preventive Cardiology · doi:10.1093/eurjpc/zwaf167 · Cited by 21 · see citing works
Xavier, A. A. D. S., da Rosa, P. P., Mackmill, L. D. B., & Roll, V. F. B. (2020) · Research in Veterinary Science · doi:10.1016/j.rvsc.2020.11.013 · Cited by 21 · see citing works
Schubert, A., Seneviratne, V., Stolz, J., Wiesmann, T., Wulf, H., Eberhart, L. H. J., & Dinges, H. (2023) · European Journal of Anaesthesiology · doi:10.1097/eja.0000000000001860 · Cited by 21 · see citing works
- Authors
- Papakonstantinou, T., Nikolakopoulou, A., Higgins, J. P. T., Egger, M., & Salanti, G.
- Affiliations
- University of Bern (CH)
- University of Bristol (GB)
- Corresponding author
- Papakonstantinou, Theodoros
- ORCID
- Venue
- Campbell Systematic Reviews
- Publisher
- SAGE Publications
- Published
- 2020-03
- Volume
- 16
- Issue
- 1
- Pages
- e1080-e1080
- ISSN
- 1891-1803 (ISSN-L)
- PMC
- PMC8356302
- OpenAlex
- W3011743134
- Semantic Scholar
- 872344f4c3bc7dae034ad2c4063dde9175294b6a
- Cited by
- 596
- References
- 27
- Citations by year
- 2026: 126 · 2025: 168 · 2024: 115 · 2023: 67 · 2022: 66 · 2021: 40 · 2020: 14
- FWCI
- 28.51
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Campbell Systematic Reviews (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Bern Open Repository and Information System (University of Bern) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Explore Bristol Research (submittedVersion, cc-by)
- Open Access CRIS of the University of Bern
- Funders
- National Science Foundation
- National Institute for Health and Care Research (NF-SI-0617-10145)
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (179158, 174281)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Social Sciences › Sociology and Political Science › Delphi Technique in Research
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- network meta-analysis, cinema, confidence estimation, risk of bias, indirectness, heterogeneity, reporting bias, imprecision, incoherence, web application
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-09.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-09.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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