Is There a “Best” Way to Detect and Minimize Publication Bias?
Bibliographic record
Abstract
Using 14 meta-analyses that included both published (n = 199) and unpublished (n = 50) randomized trials, we evaluated the utility of different analytical approaches to detect, assess robustness, and minimize publication bias in meta-analysis. The rank correlation and graphical tests indicated funnel plot asymmetry in 3 and 7 of the 14 meta-analyses, respectively. The file drawer number estimates using Iyengar-Greenhouse method were between 1.5 and 4.7 times smaller compared to Rosenthal's estimates. The median difference between the Trim and Fill estimates and the actual number of missing studies was 1 (range -4, 6). Weighted estimation methods adjusted for publication bias and provided estimates of intervention effect close to the reference standard, on average. We showed there are differences in the conclusions one would reach clinically based on the different analytical approaches dealing with publication bias. Our results also suggest that the appropriate use of these methods improves the reliability and accuracy of meta-analysis.
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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 | 0.481 | 0.702 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.024 |
| Bibliometrics | 0.019 | 0.012 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".