Publication of data sharing statements in clinical trials by cardiovascular journals: a quantitative and qualitative analysis
Bibliographic record
Abstract
Many cardiovascular disease (CVD) journals request data sharing statements upon trial report submission, but their compliance in publishing these statements remains unclear. We therefore performed a quantitative analysis to evaluate the current practice of the publications of data sharing statements in clinical trials by CVD journals, which included 78 CVD journals that published clinical trials from Jan 2019 to Dec 2022 and had data sharing statement request. Multivariable logistic regression analysis was used to examine the association between journal characteristics and journals' publications of statements. We also ran an online qualitative survey by sending anonymous questionnaires to editors-in-chief from CVD journals for their opinions on journals' publications of statements, trying to further explore why the journals did not publish statements. Their perspectives could provide in-depth information on and new insights into promoting publications of data sharing statements. This quantitative and qualitative analysis assessing the current practice of publishing data sharing statements by CVD journals, may generate new evidence to promote the actual data sharing and transparency in CVD trials.
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 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 | 0.851 | 0.425 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.002 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.010 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads 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".