Current Attitudes toward Unfunded Cancer Therapies among Canadian Medical Oncologists
Bibliographic record
Abstract
Background: Despite successes in the development of innovative anticancer therapies, the fiscal and capacity restraints of the Canadian public healthcare system result in challenges with drug access. A meaningful proportion of systemic therapies ultimately do not receive public funding despite supporting clinical evidence. In this study, we assessed Canadian medical oncologists’ current attitudes toward discussing publicly unfunded cancer treatments with patients and predictors of different practices. Methods: A web-based survey consisting of multiple choice and case-based scenarios was distributed to medical oncologists identified through the Royal College of Physicians and Surgeons of Canada directory. Results: A total of 116 responses were received. Almost all respondents reported discussing publicly unfunded treatments, including those who did so for Health Canada (HC) approved treatments (50%) and those who discussed off-label treatments (i.e., not HC approved) as guided by national guidelines (48%). Respondents in practice for over 15 years versus less than 5 years (OR 0.14, 95% CI 0.04–0.50, p = 0.002) and those who worked in a community practice versus comprehensive cancer center (OR 0.17, 95% CI 0.03–0.91, p = 0.04) were significantly less likely to discuss off-label treatment options with their patients. Almost half of respondents (47%) indicated that their institution did not permit the administration of unfunded treatments. Conclusions: There is variability in medical oncologists’ practices when it comes to discussing unfunded therapies. Given the limitations within Canada’s publicly funded healthcare system, physicians are faced with the challenge of navigating an increasingly complex balance between patient care and available resources. Engagement of relevant stakeholders and policy makers is crucial in the continued evaluation of Canada’s drug funding process.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".