Editorial-Sharing Clinical Trial Data: A Proposal from the International Committee of Medical Journal Editors
Bibliographic record
Abstract
Editors (ICMJE) believes that there is an ethical obligation to responsibly share data generated by interventional clinical trials because participants have put themselves at risk.In a growing consensus, many funders around the worldfoundations, government agencies, and industrynow mandate data sharing.Here we outline ICMJE's proposed requirements to help meet this obligation.We encourage feedback on the proposed requirements.Anyone can provide feedback at www.icmje.orgby 18 April 2016.The ICMJE defines a clinical trial as any research project that prospectively assigns people or a group of people to an intervention, with or without concurrent comparison or control groups, to study the cause-and-effect relationship between a health-related intervention and a health outcome.Further details may be found in the Recommendations for the Conduct, Reporting, Editing and Publication of Scholarly Work in Medical Journals at www.icmje.org.As a condition of consideration for publication of a clinical trial report in our member journals, the ICMJE proposes to require authors to share with others the deidentified individual-patient data (IPD) underlying the results presented in the article (including tables, figures, and appendices or supplementary material) no later than 6 months after publication.The data underlying the results are defined as the IPD required to reproduce the article's findings,
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 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 | 0.550 | 0.711 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.008 | 0.014 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.042 | 0.026 |
| Open science | 0.020 | 0.017 |
| Research integrity | 0.097 | 0.072 |
| Insufficient payload (model declined to judge) | 0.014 | 0.023 |
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".