The evolving landscape of academic and industry partnerships in gynecologic oncology clinical trials.
Bibliographic record
Abstract
9038 Background: To determine trends in the financial relationships of study investigators in gynecologic oncology trials. Methods: From 2011-2024, phase III trials were identified from clinicaltrials.gov. Data was collected from the conflict-of-interest (COI) statements of 54 clinical trials. Chi-square and Fisher's exact tests were used for statistical analysis. Results: Of1,390 total co-authors from 54 clinical trial publications, we found that 46.3% of authors were from European nations, 28.3% were based in the United States, and 16.9% were from East Asian nations, with the remaining 8.5% from other nations (including, but not limited to, Australia, Canada, and Mexico). Of the listed authors’ titles, 82.8% held MDs and 17.2% held non-MD degrees (PHD, MPH, MSc). Most (74.0%) of the trials were on ovarian cancer, with the remainder being on cervical (13.0%) and endometrial (13.0%) cancer. The majority (79.5%) of trials were sponsored by pharmaceutical companies, while the remainder were academic/cooperative-led trials. Among the pharmaceutical and industry sponsors, Roche / Genentech sponsored the most trials (27.8%), followed by Merck Sharp & Dohme (16.7%) and AstraZeneca (14.8%). Overall, 61.8% of total authors had some form of COI; of the total authors, 40.9% were consultants, 29.1% received research funding, 9.6% attended speaker bureaus, 18.6% received travel funds, and 9.8% declared employment/stock ownership. In all trials, the majority of authors disclosed COI regardless of if the trial was pharmaceutical or cooperative-led, at 63.5% and 55.2%, respectively. To evaluate trends, we divided the data into three time periods, 2011-2015, 2016-2019, and 2020-2024. Over time, there was a statistically significant increase in the number of authors who were consultants: 27% to 40% to 44% (p<0.001); who received research funding: 16% to 25% to 33% (p<0.0001); and who received travel funds: 8% to 19% to 20% (p<0.01). There was no change in speaker bureau participation (p=0.63) or employment/stock ownership (p=0.20). Conclusions: Financial relationships between study investigators and pharmaceutical companies have increased, particularly in consulting and research funding. As industry involvement grows, academic cooperative groups should maintain close collaboration to ensure scientific integrity and guide trial design.
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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 | 0.434 | 0.529 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".