Continental Divide? The Attitudes of US and Canadian Oncologists on the Costs, Cost-Effectiveness, and Health Policies Associated With New Cancer Drugs
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: Oncologists in the United States and Canada work in different health care systems, but physicians in both countries face challenges posed by the rising costs of cancer drugs. We compared their attitudes regarding the costs and cost-effectiveness of medications and related health policy. METHODS: Survey responses of a random sample of 1,355 United States and 238 Canadian medical oncologists (all outside of Québec) were compared. RESULTS: Response rate was 59%. More US oncologists (67% v 52%; P < .001) favor access to effective treatments regardless of cost, while more Canadians favor access to effective treatments only if they are cost-effective (75% v 58%; P < .001). Most (84% US, 80% Canadian) oncologists state that patient out-of-pocket costs influence their treatment recommendations, but less than half the respondents always or frequently discuss the costs of treatments with their patients. The majority of oncologists favor more use of cost-effectiveness data in coverage decisions (80% US, 69% Canadian; P = .004), but fewer than half the oncologists in both countries feel well equipped to use cost-effectiveness information. Majorities of oncologists favor government price controls (57% US, 68% Canadian; P = .01), but less than half favor more cost-sharing by patients (29% US, 41% Canadian; P = .004). Oncologists in both countries prefer to have physicians and nonprofit agencies determine whether drugs provide good value. CONCLUSION: Oncologists in the United States and Canada generally have similar attitudes regarding cancer drug costs, cost-effectiveness, and associated policies, despite practicing in different health care systems. The results support providing education to help oncologists in both countries use cost-effectiveness information and discuss drug costs with their patients.
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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 | 0.005 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it