Data sharing statements for clinical trials: a cross-sectional survey of cardiology journals
Bibliographic record
Abstract
OBJECTIVE: The International Committee of Medical Journal Editors requires data sharing statements in trial publications, but whether cardiology journals request data sharing statements in clinical trial submissions is unclear. We performed a survey to assess whether cardiology journals request data sharing statements in clinical trials. DESIGN, SETTING, DATA SOURCE AND PARTICIPANTS: All cardiac and cardiovascular systems journals that published clinical trials from January 2019 to December 2022 were included. The study outcome was journal requests for data sharing statements. Multivariable logistic regression analysis was used to examine the association between journal characteristics and journal requests. We also explored whether journal requests aligned with their subsequently published clinical trials. RESULTS: A total of 126 journals were included, among which 96 (76.2%) requested data sharing statements in clinical trials. Elsevier journals and Consolidated Standards of Reporting Trials endorsement had increased adjusted odds of requesting data sharing statements, with an OR of 5.74 (95% CI 1.45 to 22.70) and 7.21 (2.69 to 19.32), respectively. In the 78 journals that requested statements, 24 (30.8%) indeed did not publish any data sharing statement in their trial reports. CONCLUSIONS: Approximately one in four cardiology journals did not request data sharing statements on clinical trial submissions, while a substantial inconsistency existed between journal requests and the actual publications of statements in their published trial reports.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | MetaresearchScholarly communicationOpen science Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| grok | MetaresearchMeta-epidemiology (broad)Open scienceScholarly communicationResearch integrity Domain: Reproducibility · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| opus | MetaresearchOpen science Domain: Reproducibility · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.031 | 0.156 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".