A survey of U.S. and Canadian oncologists’ attitudes toward the cost, cost-effectiveness (CE), and reimbursement of cancer drugs
Bibliographic record
Abstract
9502 Background: Drug costs and reimbursement issues offer significant challenges to U.S. and Canadian oncologists even though they practice in substantially different health care systems. However, little is known about the attitudes of American and Canadian oncologists towards these issues. Methods: We surveyed 1,379 U.S. and 356 Cdn oncologists to assess their attitudes to cancer drug costs, CE and reimbursement policies. Results: Response rate was 57% in the U.S. and 48% in Canada. Oncologists in both countries stated that patients' “out-of-pocket” drug costs influenced their treatment recommendations (84% U.S., 80% Cdn respondents). Most respondents felt that every patient should have access to effective cancer treatments regardless of cost (66% US; 54% Cdn), while 59% of U.S. and 72% of Cdn and respondents believed that patients should only have access to effective cancer treatments that provided “good value for money.” 70% of U.S. and 64% Cdn respondents felt that <$100,000 per life year gained was a reasonable definition of “good value for money” but less than half of respondents (42% US, 49% Cdn) felt well prepared to interpret and use CE information in their treatment decisions. A majority of respondents (57% US, 69% Cdn) felt government price controls for cancer drugs are needed while a minority felt that more cost-sharing by patients was needed (29% US, 37% Cdn). Most oncologists felt that evaluating whether a drug provides “good value” should be overseen by an independent non-profit agency (57% US, 71% Cdn) or physicians (61% US and Cdn); in contrast, few believed that government (21% US, 33% Cdn), patients (36% US, 37% Cdn) or insurance companies (6% US, 10% Cdn) should determine “good value”. 79% of U.S. and 69% of Cdn respondents felt more use of CE data in coverage and reimbursement decisions is needed. Conclusions: Oncologists in the U.S. and Canada share many similar attitudes to cancer drug costs, CE, and reimbursement policies despite differences in their health care systems. In both countries, oncologists favor more use of CE information. No significant financial relationships to disclose.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".