How should individual participant data (IPD) from publicly funded clinical trials be shared?
Bibliographic record
Abstract
BACKGROUND: Individual participant data (IPD) from completed clinical trials should be responsibly shared to support efficient clinical research, generate new knowledge and bring benefit to patients. The Medical Research Council (MRC) Hubs for Trials Methodology Research (HTMR) has developed guidance to facilitate the sharing of IPD from publicly funded clinical trials. METHODS: Development of the guidance was completed over four phases which included a focussed review of policy documents, a web-based survey of the UK Clinical Research Collaboration (CRC) Registered Clinical Trials Units (CTU) Network, participation of an expert committee and an open consultation with the UKCRC Registered CTU Network. The project was funded by the MRC HTMR (MR/L004933/1-R39). RESULTS: Good practice principles include: (i) the use of a controlled access approach, using a transparent and robust system to review requests and provide secure data access; (ii) seeking consent for sharing IPD from trial participants in all future clinical trials with adequate assurance that patient privacy and confidentiality can be maintained; and (iii) establishing an approach to resource the sharing of IPD which would include support from trial funders, sponsor organisations and users of IPD. The guidance has been endorsed by Cancer Research UK, MRC Methodology Research Programme Advisory Group, Wellcome Trust and the Executive Group of the UKCRC Registered CTU Network. The National Institute for Health Research (NIHR) has confirmed it is supportive of the application of this guidance. CONCLUSIONS: Implementation of these principles will improve transparency, increase the coherent sharing of IPD from publicly funded trials, and help publicly funded trials to adhere to trial funder and journal requirements for data sharing.
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How this classification was reachedexpand
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 | 0.855 | 0.929 |
| Meta-epidemiology (narrow) | 0.002 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.009 | 0.050 |
| Scholarly communication | 0.047 | 0.068 |
| Open science | 0.018 | 0.039 |
| Research integrity | 0.029 | 0.025 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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; the direct Gemma label and the distilled Codex classifier 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".