Disclosure of Conflicts of Interest by Authors of Clinical Trials and Editorials in Oncology
Bibliographic record
Abstract
PURPOSE: There is concern that financial relationships between sponsors and investigators may bias research results. Our objective was to evaluate the epidemiology of conflicts of interest (COIs) among authors of clinical trials and editorials in oncology and the relationship between COI disclosure and source of funding. METHODS: We did a cross-sectional survey of clinical trials and editorials of anticancer agents and supportive care medications published in the Journal of Clinical Oncology (JCO) during a 1-year period. RESULTS: Of 1,533 articles published in JCO between January 1, 2005, and January 31, 2006, 332 met our inclusion criteria; 289 (87%) were clinical trials, and 43 (13%) were editorials. The pharmaceutical industry entirely or partially funded 44% of the clinical trials. At least one COI was disclosed in 69% of clinical trials and 51% of editorials. The most common types of COI reported by authors were consultancy fees, honoraria, and research funds. The highest monetary levels of interest reported by authors were for research grants, but the majority of authors with COIs received less than US$10,000. In multivariable analysis, authors of clinical trials conducted in North America (North America v Europe: odds ratio [OR] = 2.9, P = .002) and authors of trials funded entirely (industry only v nonprofit: OR = 13.8, P < .001) or partially (both industry and nonprofit v nonprofit only: OR = 5.8, P < .001) by industry were more likely to report personal COIs. CONCLUSION: COIs are common in clinical cancer research and usually take the form of consultancy fees, honoraria, and research funds. Source of study funding was significantly associated with COI disclosure.
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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 | 0.133 | 0.433 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".