Payments by US pharmaceutical and medical device manufacturers to US medical journal editors: retrospective observational study
Bibliographic record
Abstract
Objective To estimate financial payments from industry to US journal editors. Design Retrospective observational study. Setting 52 influential (high impact factor for their specialty) US medical journals from 26 specialties and US Open Payments database, 2014. Participants 713 editors at the associate level and above identified from each journal’s online masthead. Main outcome measures All general payments (eg, personal income) and research related payments from pharmaceutical and medical device manufacturers to eligible physicians in 2014. Percentages of editors receiving payments and the magnitude of such payments were compared across journals and by specialty. Journal websites were also reviewed to determine if conflict of interest policies for editors were readily accessible. Results Of 713 eligible editors, 361 (50.6%) received some (>$0) general payments in 2014, and 139 (19.5%) received research payments. The median general payment was $11 (£8; €9) (interquartile range $0-2923) and the median research payment was $0 ($0-0). The mean general payment was $28 136 (SD $415 045), and the mean research payment was $37 963 (SD $175 239). The highest median general payments were received by journal editors from endocrinology ($7207, $0-85 816), cardiology ($2664, $0-12 912), gastroenterology ($696, $0-20 002), rheumatology ($515, $0-14 280), and urology ($480, $90-669). For high impact general medicine journals, median payments were $0 ($0-14). A review of the 52 journal websites revealed that editor conflict of interest policies were readily accessible (ie, within five minutes) for 17/52 (32.7%) of journals. Conclusions Industry payments to journal editors are common and often large, particularly for certain subspecialties. Journals should consider the potential impact of such payments on public trust in published research.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".