The Prioritization Preferences of Pan-Canadian Oncology Drug Review Members and the Canadian Public: A Stated-Preferences Comparison
Bibliographic record
Abstract
The pan-Canadian Oncology Drug Review (pcodr) is responsible for making coverage recommendations to provincial and territorial drug plans about cancer drugs. Within the pcodr process, small groups of experts (including public representatives) consider the characteristics of each drug and make a funding recommendation. It is important to understand how the values and preferences of those decision-makers compare with the values and preferences of the citizens on whose behalf they are acting. In the present study, stated preference methods were used to elicit prioritization preferences from a representative sample of the Canadian public and a small convenience sample of pcodr committee members. The results suggested that neither group sought strictly to maximize quality-adjusted life year (qaly) gains and that they were willing to sacrifice some efficiency to prioritize particular patient characteristics. Both groups had a significant aversion to prioritizing older patients, patients in good pre-treatment health, and patients in poor post-treatment health. Those results are reassuring, in that they suggest that pcodr decision-maker preferences are consistent with those of the Canadian public, but they also imply that, like the larger public, decision-makers might value health gains to some patients more or less highly than the same gains to others. The implicit nature of pcodr decision criteria means that the acceptability or limits of such differential valuations are unclear. Likewise, there is no guidance as to which potential equity factors-for example, age, initial severity, and so on-are legitimate and which are not. More explicit guidance could improve the consistency and transparency of pcodr recommendations.
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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 | 0.019 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".