Managing the incidence of selective reporting bias: a survey of Cochrane review groups
Bibliographic record
Abstract
BACKGROUND: Selective reporting bias (SRB), the incomplete publication of outcomes measured or of analyses performed in a study, may lead to the over- or underestimation of treatment effects or harms. Cochrane systematic reviews of interventions are required to assess the risk of SRB, achieved in part by applying the Cochrane risk of bias tool to each included randomised trial. The Cochrane Handbook outlines strategies for a comprehensive risk of bias assessment, but the extent to which these are followed by Cochrane review groups (CRGs) has not been assessed to date. The objective of this study was to determine the methods which CRGs require of their authors to address SRB within systematic reviews, and how SRB risk assessments are verified. METHODS: A cross-sectional survey was developed and distributed electronically to the 52 CRGs involved in intervention reviews. RESULTS: Responses from 42 CRGs show that the majority refer their authors to the Cochrane Handbook for specific instruction regarding assessments of SRB. The handbook strategies remain variably enforced, with 57 % (24/42) of CRGs not requiring review authors to search for included trial protocols and 31 % (13/42) not requiring that contact with individual study authors be attempted. Only half (48 %, 20/42) of the groups consistently verify review authors' assessments of the risk of SRB to ensure completeness. CONCLUSIONS: A range of practices are used by CRGs for addressing SRB, with many steps outlined in the Cochrane Handbook being encouraged but not required. The majority of CRGs do not consider their review authors to be sufficiently competent to assess for SRB, yet risk of bias assessments are not always verified by editors before publication. The implications of SRB may not be fully appreciated by all CRGs, and resolving the identified issues may require an approach targeting several steps in the systematic review process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.840 | 0.930 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.054 | 0.041 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.018 | 0.027 |
| Open science | 0.011 | 0.026 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".