Development of a combined database for meta‐epidemiological research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.922 | 0.879 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 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 it