High reprint orders in medical journals and pharmaceutical industry funding: case-control study
Bibliographic record
Abstract
OBJECTIVES: To assess the extent to which funding and study design are associated with high reprint orders. DESIGN: Case-control study. SETTING: Top articles by size of reprint orders in seven journals, 2002-09. PARTICIPANTS: Lancet, Lancet Neurology, Lancet Oncology (Lancet Group), BMJ, Gut, Heart, and Journal of Neurology, Neurosurgery & Psychiatry (BMJ Group) matched to contemporaneous articles not in the list of high reprint orders. MAIN OUTCOME MEASURES: Funding and design of randomised controlled trials or other study designs. RESULTS: Median reprint orders for the seven journals ranged from 3000 to 126,350. Papers with high reprint orders were more likely to be funded by the pharmaceutical industry than were control papers (industry funding versus other or none: odds ratio 8.64, 95% confidence interval 5.09 to 14.68, and mixed funding versus other or none: 3.72, 2.43 to 5.70). CONCLUSIONS: Funding by the pharmaceutical industry is associated with high numbers of reprint orders.
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 | 0.022 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".