Development of a combined database for meta‐epidemiological research
Bibliographic record
Abstract
Collections of meta-analyses assembled in meta-epidemiological studies are used to study associations of trial characteristics with intervention effect estimates. However, methods and findings are not consistent across studies. To combine data from 10 meta-epidemiological studies into a single database, and derive a harmonized dataset without overlap between meta-analyses. The database design allowed trials to be contained in different meta-analyses, multiple meta-analyses in systematic reviews, overlapping meta-analyses between systematic reviews, and multiple references to the same trial or review. Unique identifiers were assigned to each reference and used to identify duplicate trials. Sets of meta-analyses with overlapping trials were identified and duplicates removed. Overlapping trials were used to examine agreement between assessments of trial characteristics. The combined database contained 427 reviews, 454 meta-analyses and 4874 trial results. Of these, 258 meta-analyses were unique, while for 196 at least one trial overlapped with another meta-analysis. Median kappa statistics for reliability of assessments were 0.60 for sequence generation, 0.58 for allocation concealment and 0.87 for blinding. Based on inspection of sets of overlapping meta-analyses, 91 meta-analyses containing 1344 trial results were removed. Additionally, 24 duplicated trial results were removed from 16 meta-analyses, to derive a final database containing 363 meta-analyses and 3477 unique trial results. The final database will be used to examine the combined evidence on sources of bias in randomized controlled trials. The strategy used to remove overlap between meta-analyses may be of use for future empirical research. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.128 | 0.344 |
| Meta-epidemiology (narrow) | 0.003 | 0.007 |
| Meta-epidemiology (broad) | 0.014 | 0.016 |
| Bibliometrics | 0.046 | 0.054 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.010 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.039 | 0.011 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".