The ICMJE and URM: Providing Independent Advice for the Conduct of Biomedical Research and Publication
Bibliographic record
Abstract
The International Committee of Medical Journal Editors (ICMJE) is a working group of editors of selected medical journals that meets annually. Founded in Vancouver, Canada, in 1978, it currently consists of 11 member journals and a representative of the US National Library of Medicine. The major purpose of the Committee is to address and provide guidance for the conduct and publishing of biomedical research and the ethical tenets underpinning these activities. This advice is detailed in the Committee's Uniform Requirements for Manuscripts Submitted to Biomedical Journals: Writing and Editing for Biomedical Publication (URM).Recently, the ICMJE has adopted an interventionist role to ensure transparency of conflict of interest revelations in the conduct and publication of industry supported research. It also pursues a policy for the lodgement with trial registries of specified details of Phase III clinical trials. Failure to comply would jeopardise publication of trial outcomes in ICMJE member journals. This policy has resulted in the coming on stream of trial registries, international agreement on trial minimal datasets and compliance with trial registration requirements.
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 | 0.312 | 0.763 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.003 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.029 | 0.013 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.030 | 0.028 |
| Insufficient payload (model declined to judge) | 0.036 | 0.069 |
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".