Characterization of industry relationships in oncology
Bibliographic record
Abstract
BACKGROUND: Collaborative relationships between academic oncology and industry (pharmaceutical, biotechnology, "omic," and medical device companies) are essential for therapeutic development in oncology; however, limited research on engagement in and perceptions of these relationships has been done. METHODS: Survey questions were developed to evaluate relationships between academic oncology and industry. An electronic survey was delivered to 1000 randomly selected members of the American Society of Clinical Oncology, a professional organization for oncologists, eliciting respondents' views around oncology-industry collaborations. The responses were analyzed according to prespecified plans. RESULTS: There were 225 survey respondents. Most were from the United States (70.0%), worked at an academic institution (60.1%), worked in medical oncology (81.2%), and had an active relationship with industry (85.8%). One quarter (26.7%) of respondents reported difficulty establishing a relationship with industry collaborators, and most respondents (75%) did not report having had mentorship in developing these relationships. The majority (85.3%) of respondents considered these collaborations important to their careers. Respondents generally thought that scientific integrity was preserved (92%), and most respondents (95%) had little concern over the quality of the collaborative product. Many (60%) shared concerns over potential conflict of interest if an individual with a compensated relationship promoted an industry product for clinical care/research, yet most respondents (67%) stated these relationships did not shape their interactions with patients. CONCLUSIONS: This study provides novel data characterizing the nature of collaborative relationships between clinicians, researchers, and industry in oncology. Although respondents considered these collaborations an important part of clinical and academic oncology, formal education or mentorship around these relationships was rare. Conflicting findings around conflict of interest highlight the importance of more dedicated research in this area. PLAIN LANGUAGE SUMMARY: Business enterprises in health care play a central role in cancer research and care, driving the development of new medical testing, drugs, and devices. Effective working relationships among clinicians, researchers, and these industry partners can promote innovative research and enhance patient care. Study of these collaborations has been limited to date. Through distribution of a questionnaire to cancer clinicians and researchers, we found that most participants consider these relationships valuable, though they find establishing such relationships challenging partly because of gaps in educational programs in this area. Our findings also highlight the need for further policy around the potential bias these relationships can introduce.
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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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".