Individual participant data meta-analyses (IPDMA): data contribution was associated with trial corresponding author country, publication year, and journal impact factor
Bibliographic record
Abstract
OBJECTIVES: The objectives were to determine the proportion of eligible randomized controlled trials (RCTs) that contributed data to individual participant data meta-analyses (IPDMAs) and explore associated factors. STUDY DESIGN AND SETTING: IPDMAs with ≥10 eligible RCTs were identified by searching MEDLINE, EMBASE, CINAHL, and Cochrane May 1, 2015 to February 13, 2017. Mixed-effect logistic regression was used to identify factors associated with data contribution. RESULTS: Of 774 eligible RCTs from 35 included IPDMAs, 517 (67%, 95% confidence interval [CI]: 63%-70%) contributed data. Compared to RCTs from journals with low-impact factors (0-2.4), RCTs from journals with higher impact factors were more likely to contribute data: impact factor 5.0-9.9, odds ratio [OR] 2.6, 95% CI: 1.37-4.86; impact factor: 10.0-19.9, OR: 5.7, 95% CI: 3.0-10.8; impact factor >20.0, OR: 4.6, 95% CI: 1.9-11.4. RCTs from the United Kingdom were more likely to contribute data than those from the United States (reference; OR: 2.4, 95% CI, 1.3-4.6). There was an increase in OR per publication year (OR: 1.05, 95% CI: 1.02-1.09). CONCLUSION: The country where RCTs are conducted, impact factor of the journal where RCTs are published, and RCT publication year were associated with data contribution in IPDMAs with ≥10 eligible RCTs.
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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 | 0.156 | 0.403 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.072 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".