Patient and Provider Perspectives on Enrollment in Precision Oncology Research: Qualitative Ethical Analysis
Bibliographic record
Abstract
BACKGROUND: The genomic frontier continues to revolutionize the practice of oncology. Advances in cancer biology from tumorigenesis to treatment resistance are driven by the molecular underpinnings of malignancy. The framing of precision oncology as both a clinical and research tool is constantly evolving and directly influences conversations between oncologists and their patients. Prior research has shown that patient-participants often have unmet or unrealistic expectations regarding the clinical utility of oncology research and genomic sequencing. This indicates the need for more in-depth investigation of how and why patients choose to participate in such research. OBJECTIVE: This study presents a qualitative ethical analysis to better understand patient and provider perspectives on enrollment in precision oncology research. METHODS: Paired semistructured interviews were conducted with patient-participants enrolled in a prospective head and neck precision oncology research platform, along with their oncology providers, at a National Cancer Institute-designated academic cancer center. RESULTS: There were three major themes that emerged from the analysis. (1) There are distinct and unique challenges with informed consent to precision medicine, chiefly involving the ability of both patient-participants and providers to effectively understand the science underlying the research. (2) The unique benefits of precision medicine enrollment are of paramount importance to patients considering enrollment. (3) Patient-participants have little concern for the risks of research enrollment, particularly in the context of a low-burden protocol. CONCLUSIONS: Patient-participants and their providers offer complementary and nuanced perspectives on their motivation to engage in precision oncology research. This reflects both the inherent promise and enthusiasm within the field, as well as the limitations and challenges of ensuring that both patient-participants and clinicians understand the complexities of the science involved.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".