Characterization of industry relationships in oncology.
Bibliographic record
Abstract
11025 Background: Collaborative relationships between academic oncology and the pharmaceutical industry are essential for therapeutic development in oncology. Despite this, formal training and mentorship in developing productive industry collaborations are not routinely included in oncology training. Since little research has been done to characterize and optimize the efficiency of these relationships, we sought to better understand the nature of such collaborations in order to identify areas for optimization. Methods: An electronic survey was administered to 1000 randomly selected ASCO members. The survey included 23 questions eliciting demographic and practice information, and 26 questions eliciting respondents’ views around oncology-industry collaborations. Survey results were analyzed using descriptive statistics. Results: There were 225 survey respondents. Most were from the United States (70%), worked at an academic institution (60.1%), worked in medical oncology (81.2%), and had an active relationship with industry (85.8%). 26.7 % of respondents reported difficulty establishing a relationship with industry collaborators. Many relied on federal (39.5%) or departmental (30.2%) funding to supplement their research ventures. Partnerships were initiated by the respondents themselves (34.6%) or industry partners (31.9%) with similar frequency, whereas institutional affiliations (15.7%) and collaborative groups (5.8%) were reported as less common means for establishing collaborations. The majority (85.3%) of respondents stated these collaborations were of importance to their career. Inclusion in industry sponsored trials (71.1%) and commitment to research funding (66.3%) were considered early signs of a productive relationship, whereas lack of effective communication (86.1%) or little engagement by senior industry leadership (63.1%) were early red flags. Most respondents (75%) did not report having had mentorship in developing these relationships. Scientific integrity was generally thought to be preserved (92%) and there was little concern over the quality of the collaborative product (95%). Many shared concern over potential conflict of interest if a compensated relationship promoted an industry product for clinical care/research (60%), yet also stated these relationships did not shape their interactions with patients (67%). Conclusions: This study provides novel data characterizing the nature of collaborative industry-academia relationships in oncology. While respondents considered these collaborations an important part of clinical and academic oncology, formal education or mentorship around these relationships is rare. Further study exploring the structure of effective industry collaborations, optimizing methods to provide education in this area at all career stages, navigating conflict of interest issues in these relationships, and understanding industry perspectives is warranted.
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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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".