Societal Preferences for Trade-offs between Cancer and Non-Cancer Health Outcomes: A Choice Experiment
Bibliographic record
Abstract
Background: Resource allocation decisions are made based on the principle of maximizing population health (efficiency). However, in practice, much higher willingness-to-pay thresholds are used for cancer therapies, with limited supportive evidence. Objective: To quantify Canadian public preferences on trade-offs between cancer and non-cancer health outcomes. Methods: Our systematic review identified 7 studies, but none evaluated cancer trade-offs. We conducted a survey using a sample of 300 respondents, with three resource allocation scenarios: (1) cancer versus non-cancer; (2) lung cancer versus heart failure; and (3) lung cancer prevention versus diabetes prevention.\nResults: The median respondent preferred health maximization, irrespective of the health condition. Across scenarios 1/2/3, only 29%, 10%, and 26%, respectively, were willing to trade-off efficiency to prioritize cancer outcomes. Regression analysis did not find any significant associations. Conclusion: We did not find evidence to support a higher preference for sacrificing total health to improve cancer outcomes over non-cancer outcomes.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".