Predictors of clinical trial data sharing: exploratory analysis of a cross-sectional survey
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: A number of research funders, biomedical journals, pharmaceutical companies, and regulatory agencies have adopted policies advocating or mandating that clinical trialists share data with external investigators. We therefore sought to determine whether certain characteristics of trialists or their trials are associated with more unfavorable perceptions of data sharing. To date, no prior research has addressed this issue. METHODS: We conducted an exploratory analysis of responses to a cross-sectional, web-based survey. The survey sample consisted of trialists who were corresponding authors of clinical trials published in 2010 or 2011 in one of six general medical journals with the highest impact factors in 2011. The following key characteristics were examined: trialists' academic productivity and geographic location, trial funding source and size, and the journal in which it was published. Main outcome measures included: support for data sharing in principle, concerns with data sharing through repositories, and reasons for granting or denying requests. Chi-squared tests and Fisher's exact tests were used to assess statistical significance. RESULTS: Of 683 potential respondents, 317 completed the survey (response rate 46%). Both support for data sharing and reporting of specific concerns with sharing data through repositories exceeded 75%, but neither differed by trialist or trial characteristics. However, there were some significant differences in explicit reasons to share or withhold data. Respondents located in Western Europe more frequently indicated they have or would share data in order to receive academic benefits or recognition when compared with respondents located in the United States or Canada (58 versus 31%). In addition, respondents who were the most academically productive less frequently indicated they have or would withhold data in order to protect research subjects when compared with less academically productive respondents (24 versus 40%), as did respondents who received industry funding when compared with those who had not (24 versus 43%). CONCLUSIONS: Respondents indicated strong support for data sharing overall. There were few notable differences in how trialists viewed the benefits and risks of data sharing when categorized by trialists' academic productivity and geographic location, trial funding source and size, and the journal in which it was published.
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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 | 0.154 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.011 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it