Re-use of trial data in the first 10 years of the data-sharing policy of the Annals of Internal Medicine: a survey of published studies
Bibliographic record
Abstract
ABSTRACT Background The Annals of Internal Medicine (AIM) has adopted a policy encouraging data-sharing since 2007. Objective To explore the impact of the AIM data-sharing policy for randomized controlled trials (RCTs) in terms of output from data-sharing (i.e. publications re-using the data). Design Retrospective study. Setting AIM. Participants RCTs published in the AIM between 2007 and 2017 were retrieved on PubMed. Publications where the data had been re-used were identified on Web of Science. Searches were performed by two independent reviewers. Interventions Intention to share data (or not) expressed in a data-sharing statement. Measurements The primary outcome was any published re-use of the data (i.e. re-analysis, secondary analysis, or meta-analysis of individual participant data [MIPD]), where the first, last and corresponding authors were not among the authors of the RCT. Components of the primary outcome and analyses without any author restriction were secondary outcomes. Analyses used Cox (primary analysis) models adjusting for RCT characteristics. Results 185 RCTs were identified. 106 (57%) mentioned willingness to share data and 79 (43%) did not. 208 secondary analyses, 67 MIPD and no re-analyses were identified. No significant association was found between intent to share and re-use where the first, last and corresponding authors were not among the authors of the primary RCT (adjusted hazard ratio = 1.04 [0.47-2.30]). Secondary outcomes also showed no association between intent to share and re-use. Limitations Possibility of residual confounding and limited power. Conclusion Over ten years, RCTs published in AIM expressing an intention to share data were not associated with more extensive re-use of the data. Registration https://osf.io/8pj5e/ Funding Source Grants from the Fondation pour la Recherche Médicale, Région Bretagne, and French National Research Agency.
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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 | 0.505 | 0.824 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| 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".