Reporting quality and risk of bias of Cochrane individual participant data meta‐analyses: A cross‐sectional study
Bibliographic record
Abstract
OBJECTIVES: This study aimed to assess the reporting quality and risk of bias of Cochrane individual participant data meta-analyses (IPD-MAs). METHODS: We searched the Cochrane Library and identified the Cochrane IPD-MAs. We used the Preferred Reporting Items for Systematic Review and Meta-Analyses of individual participant data (PRISMA-IPD) assessed the reporting quality of included Cochrane IPD- MAs, and the Risk Of Bias In Systematic reviews (ROBIS) tool was used to assess the risk of bias. We performed stratified and correlation analyses to explore factors affecting the quality. RESULTS: Forty-six Cochrane IPD-MAs were included in our study. Twenty-six Cochrane IPD-MAs (56.5%) had statistical or epidemiological authors involved, and 31 (67.4%) contained only IPD data. Thirty-five studies (76.1%) did not report whether they used 1-stage or 2-stage methods, and forty (87.0%) did not report the statistical techniques used for missing participant data. We found that the entire compliance reported PRISMA-IPD items of Cochrane IPD-MAs published after 2015 (n = 18; Mean ± SD: 26.61 ± 2.75) was higher than those studies published in 2015 and before (n = 28; Mean ± SD: 22.61 ± 4.73), the difference was statistically significant (p = 0.002). A strong positive correlation was found between the fully reported PRISMA-IPD items and fully accordance ROBIS items (Spearman's: ρ = 0.653, p < 0.001). CONCLUSIONS: The quality of Cochrane IPD-MAs is not high, especially in the reporting of statistical methods. There was room for further improvement in IPD retrieval, IPD integrity and statistical analyses.
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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 | 0.492 | 0.741 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.022 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".