Clinical trial data sharing: here’s the challenge
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Anonymised patient-level data from clinical research are increasingly recognised as a fundamental and valuable resource. It has value beyond the original research project and can help drive scientific research and innovations and improve patient care. To support responsible data sharing, we need to develop systems that work for all stakeholders. The members of the Independent Review Panel (IRP) for the data sharing platform Clinical Study Data Request (CSDR) describe here some summary metrics from the platform and challenge the research community on why the promised demand for data has not been observed. SUMMARY OF DATA: From 2014 to the end of January 2019, there were a total of 473 research proposals (RPs) submitted to CSDR. Of these, 364 met initial administrative and data availability checks, and the IRP approved 291. Of the 90 research teams that had completed their analyses by January 2018, 41 reported at least one resulting publication to CSDR. Less than half of the studies ever listed on CSDR have been requested. CONCLUSION: While acknowledging there are areas for improvement in speed of access and promotion of the platform, the total number of applications for access and the resulting publications have been low and challenge the sustainability of this model. What are the barriers for data contributors and secondary analysis researchers? If this model does not work for all, what needs to be changed? One thing is clear: that data access can realise new and unforeseen contributions to knowledge and improve patient health, but this will not be achieved unless we build sustainable models together that work for all.
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.
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 | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it