Conflict of Interest Disclosure in Oncology: Preliminary Insights From the Global ONCOTRUST-1 Cross-Sectional Study
Bibliographic record
Abstract
PURPOSE: Conflicts of interest (COIs) between oncologists and industry might considerably influence how the presentation of the research results is delivered, ultimately affecting clinical decisions and policy-making. Although there are many regulations on reporting COI in high-income countries (HICs), little is known about their reporting in low- and middle-income countries (LMICs). Oncology Transparency Under Scrutiny and Tracking (ONCOTRUST-1) is a pilot global survey to explore the knowledge and perceptions of oncologists regarding COI. MATERIALS AND METHODS: We designed an online 27-question-based survey in the English language to explore the perceptions and knowledge of oncologists regarding COI, with an emphasis on LMICs. Descriptive statistics and the Consensus-Based Checklist for Reporting of Survey Studies guidelines were used to report the findings. RESULTS: ONCOTRUST-1 surveyed 200 oncologists, 70.9% of them practicing in LMICs. Median age of the respondents was 36 (range, 26-84) years; 47.5% of them were women. Of the respondents, 40.5% reported weekly visits by pharmaceutical representatives to their institutions. Regarding oncologists' perceptions of COI that require disclosure, direct financial benefits, such as honoraria, ranked highest (58.5%), followed by gifts from pharmaceutical representatives (50%) and travel grants for attending conferences (44.5%). By contrast, personal or institutional research funding, sample drugs, consulting or advisory board, expert testimony, and food and beverage funded by pharmaceutical industry were less frequently considered as COI. Moreover, only 24% of surveyed oncologists could correctly categorize all situations representing a COI. CONCLUSION: These findings underscore the importance of clear guidelines, education, and transparency in reporting COI in oncology. This hypothesis-generating pilot survey provided the rationale for ONCOTRUST-2 study, which will compare perceptions of COI among oncologists in LMICs and HICs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".