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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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; both teacher heads 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".