Personal financial conflicts of interest among authors of cancer clinical trials and editorials: scope and relationship to study funding
Bibliographic record
Abstract
948 Background: The growing involvement of the pharmaceutical industry in clinical cancer research has led to concerns regarding scientific integrity of the research enterprise. Here we describe the epidemiology of potential personal financial conflicts of interest (COI) disclosed by authors of clinical cancer studies. Methods: We reviewed all clinical trials (CTs) and editorials of systemic anticancer and supportive care drugs published in the Journal of Clinical Oncology in a one-year period. We used summary statistics to describe the results and logistic regression to identify factors associated with COI among authors of CTs (source of funding, type of study, continent(s) where the study was conducted and number of authors). The dependent variable was disclosure of at least one COI (any type) per article. Results: Of the 1,533 articles published betweenJan 2005 and Jan 2006, 332 met our inclusion criteria: 289 (87%) were CTs and 43 (13%) were editorials. At least one COI (any type) was disclosed in 198/289 (69%) CTs and in 22/43 (51%) editorials. The pharmaceutical industry partially or entirely funded 129/289 (45%) CTs. The most common types of COI disclosed by authors were honoraria, consultancy fees and research funds. In a sample of 177 articles published since August 2005, when amount of money received began being reported, the largest monetary levels were reported for research funds. In multivariate analysis, authors of CTs originating in North America (North America vs. Europe: OR=2.9, p=0.002) and those funded entirely (industry vs non-profit organization: OR=13.8, p Conclusions: Personal financialCOI are common among authors of clinical cancer studies and usually take the form of honoraria, consultancy fees and/or research funds. Source of funding is significantly associated with disclosure of COI.
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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 | 0.198 | 0.592 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.021 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".