Role of Comparative Effectiveness Research in Cancer Funding Decisions in Ontario, Canada
Bibliographic record
Abstract
Recently, the evidence-based drug funding process in Ontario, Canada, was challenged by a young mother with a breast tumor too small, based on the evidence that existed at the time, to qualify for an expensive drug. In reality, this is only the latest in a number of challenges the publicly funded health care system has had to deal with in the face of an evolving drug policy landscape. This article defines comparative effectiveness research (CER), considering how it is viewed differently in the United States and Canada. It also reviews the role CER now plays in the Ontario drug funding process and concludes with a review of the challenges and opportunities of using observational data to conduct CER and incorporate it into policy making within a universal health care system. Many of the issues faced by Ontario are relevant beyond Canada, including in the United States during this period of health care reform.
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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 | 0.081 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".