Comparative Effectiveness Research in Ontario, Canada: Producing Relevant and Timely Information for Health Care Decision Makers
Bibliographic record
Abstract
CONTEXT: Comparative effectiveness research is increasingly being recognized as a method to link research with the information needs of decision makers. As the United States begins to invest in comparative effectiveness, it would be wise to look at other functioning research networks to understand the infrastructure and funding required to support them. METHODS: This case study looks at the comparative effectiveness research network in Ontario, Canada, for which a neutral coordinating committee is responsible for prioritizing topics, assessing evidence, providing recommendations on coverage decisions, and determining pertinent research questions for further evaluation. This committee is supported by the Medical Advisory Secretariat and several large research institutions. This article analyzes the infrastructure and cost needed to support this network and offers recommendations for developing policies and methodologies to support comparative effectiveness research in the United States. FINDINGS: The research network in place in Ontario explicitly links decision making with evidence generation, in a transparent, timely, and efficient way. Funding is provided by the Ontario government through a reliable and stable funding mechanism that helps ensure that the studies it supports are relevant to decision makers. CONCLUSIONS: With the recent allocation of funds to support comparative effectiveness research from the American Recovery and Reinvestment Act, the United States should begin to construct an infrastructure that applies these features to make sure that evidence generated from this effort positively affects the quality of health care delivered to patients.
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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 | 0.042 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".