Evaluating alignment between Canadian Common Drug Review reimbursement recommendations and provincial drug plan listing decisions: an exploratory study
Bibliographic record
Abstract
BACKGROUND: The CADTH Common Drug Review was established in 2002 to prepare national health technology assessment reports to guide listing decisions for 18 participating drug plans. The aim of this study was to compare the nonmandatory recommendations from the Common Drug Review in Canada with the listing decisions of provincial payers to determine alignment. METHODS: We identified the recommendations issued by the Common Drug Review from Jan. 1, 2009, to Jan. 1, 2015, and compared these with the listing decisions of 3 provincial public payers (Alberta, British Columbia and Ontario) that participate in the Common Drug Review and the recommendations from Quebec. RESULTS: We identified 174 medicine-indication pairs in CADTH Common Drug Review reports issued from Jan. 1, 2009, to Jan. 1, 2015; 110 of these met the inclusion criterion. Among the 110 medicine-indication pairs, listing decisions were available for 95 in Alberta, 102 in Quebec, 104 in Ontario and 106 in BC. There was moderate to substantial agreement between provincial listing decisions and Common Drug Review recommendations: 74.5% (κ = 0.47, 95% confidence interval [CI] 0.31-0.64) for Quebec, 78.8% (κ = 0.56, 95% CI 0.41-0.72) for Ontario, 78.9% (κ = 0.58, 95% CI 0.42-0.74) for Alberta and 81.1% (κ = 0.62, 95% CI 0.47-0.77) for BC. INTERPRETATION: Our study showed moderate to substantial agreement between Common Drug Review recommendations and provincial listing decisions. Future studies can build on this research by evaluating the concordance between Common Drug Review recommendations and listing decisions of all participating federal, provincial and territorial drug plans.
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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 | 0.060 | 0.249 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".