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Record W3036659527 · doi:10.1101/2020.06.16.20132894

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

2020· preprint· en· W3036659527 on OpenAlexaff
Claude Pellen, Laura Caquelin, Alexia Jouvance-Le Bail, Jeanne Fabiola Gaba, Mathilde Vérin, David Moher, John P. A. Ioannidis, Florian Naudet

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsOttawa Hospital
FundersRégion BretagneFondation pour la Recherche MédicaleAgence Nationale de la Recherche
KeywordsRandomized controlled trialMedicineAnnalsConfoundingMeta-analysisHazard ratioData sharingPsychological interventionFamily medicineAlternative medicineInternal medicineConfidence intervalGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.505
metaresearch head score (Gemma)0.824
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.495
Threshold uncertainty score0.611

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5050.824
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0040.008
Bibliometrics0.0200.020
Science and technology studies0.0010.005
Scholarly communication0.0100.017
Open science0.0050.009
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.940
GPT teacher head0.608
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designObservational
DomainReproducibility
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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