Recommendations to improve adverse event reporting in clinical trial publications: a joint pharmaceutical industry/journal editor perspective
Bibliographic record
Abstract
Medical Publishing Insights & Practices (MPIP)—a partnership among pharmaceutical companies and the International Society for Medical Publication Professionals—aims to identify ways to improve transparency and credibility in publishing the results of industry sponsored research. This article provides guidance from MPIP on clinically relevant and more informative adverse event reporting, previously identified by journal editors as a significant unmet need to improve patient care and increase the credibility of industry sponsored publications. Our recommendations include highlighting adverse events of most relevance to practitioners and their patients, avoiding broad summary statements such as “generally safe” or “well tolerated,” and including more detailed adverse event data (where appropriate) to offer additional clinically important insight. These recommendations complement the earlier recommendations in the Consolidated Standards of Reporting Trials (CONSORT) Harms Extension. Although developed for industry sponsored trials, the adoption of our recommendations would enhance adverse event reporting in clinical research publications regardless of the funding source and thereby facilitate clinical decision making.
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 | 0.597 | 0.847 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.016 | 0.021 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.037 | 0.038 |
| Open science | 0.015 | 0.013 |
| Research integrity | 0.069 | 0.074 |
| Insufficient payload (model declined to judge) | 0.007 | 0.018 |
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".