Decision-making by surgeons about referral for adjuvant therapy for patients with non-small-cell lung, breast or colorectal cancer: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Because surgeons are the main gatekeepers to oncology services, understanding how they make decisions related to referral for adjuvant therapies is important to optimize referral rates and use of oncology services for patients with potentially curable disease. We examined decision-making by surgeons related to referral to oncology services for patients having undergone curative-intent surgery for non-small-cell lung, breast or colorectal cancer. METHODS: We conducted a qualitative study, whose design was guided by the principles of grounded theory. Semi-structured interviews were held with 29 surgeons who performed non-small-cell lung, breast or colorectal cancer surgery in the province of Nova Scotia. Data were collected and analyzed concurrently. Analysis involved an inductive, grounded approach using constant comparative analysis. Data collection and analysis continued until theoretical saturation was reached. RESULTS: Seven factors influenced the surgeons' decision-making related to referral to oncology services: indications and contraindications for therapy; patients' beliefs and preferences; a belief that oncologists are the experts; knowledge of local standards of care; consultation with oncology colleagues; navigating patient logistics (e.g., lodging, caregiving responsibilities, insurance coverage); and system resources and capacity. INTERPRETATION: Our study's findings provide a novel understanding of how surgeons make decisions about oncology referral and point to potential areas for intervention to promote referral to oncology services for patients for whom adjuvant therapy is recommended.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".