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 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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".