Human research review committee requirements in medical journals
Bibliographic record
Abstract
PURPOSE: Independent ethical review committees safeguard participants in human research. The purpose of this study was to describe the current ethical guidelines for human research requirements in the Instructions to Authors of the English language medical journals previously studied in 1995. METHODS: The instructions to authors of English language medicine journals from the Abridged Index Medicus were searched for any policies regarding guidance on the ethical treatment of human subjects in research. RESULTS: More medical journals require independent ethics committee approval of human research now [84/101 (83%)] than 10 years ago [48/102 (47%) (P < 0.001)], and most journals continue to require that this disclosure appear in the manuscript [71/84 (85%) vs. then 37/48 (77%) (P=0.29)]. Fewer medical journal instructions to authors provide no ethical guidelines for human research now [8/101 (8%)] than 10 years ago [25/102 (24%) (P < 0.001)]. No journal required submission of the study approval letter or of the approved protocol. CONCLUSIONS: Although medicine journals increasingly require disclosure statements of independent ethics committee approval for human research, they fail to verify such approval beyond taking authors for their word.
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 | 0.379 | 0.558 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.013 |
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".