Industry Sponsorship and Authorship of Clinical Trials Over 20 Years
Bibliographic record
Abstract
BACKGROUND: The pharmaceutical industry has become a major source of funding for biomedical research. Our general observation is that pharmaceutical industry employees are appearing with increasing frequency as coauthors of clinical trial publications. OBJECTIVE: To characterize clinical trial funding, reporting, and sources; investigate author-industry affiliation; and describe clinical outcome trends over time. METHODS: We reviewed 500 randomly selected clinical trials published in 5 influential medical journals over a 20-year period (1981-2000). RESULTS: Of the 500 clinical trials reviewed, 181 (36%) involved pharmaceutical industry as an independent (n = 104) or joint (n = 77) sponsor and 180 (36%) involved a peer-review funding source; the balance (139; 28%) lacked any declared sponsorship. The percentage of industry-sponsored clinical trials increased to 62% during 1997-2000. The percentage of nonprofit sponsored clinical trials remained constant over time, while the percentage of those without funding declaration declined. Reported author affiliation with industry increased to 66% of clinical trials sponsored only by industry. An increase in the percentage of clinical trials with reported author-industry affiliation was observed for all journals. Regardless of funding source, the majority of clinical trials reported clinical outcomes that favored the study drug. CONCLUSIONS: Pharmaceutical industry-sponsored and mixed-funding clinical trials are common, and the relative incidence of published trials with these declared funding sources in the 5 journals reviewed has increased. Industry employees are appearing as coauthors of clinical trial publications with increasing frequency.
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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 | 0.037 | 0.167 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".