Abstract 22: Impact Of Drug Policy On Regional Trends In Ezetimibe Use
Bibliographic record
Abstract
Background: Ezetimibe use has gradually but steadily increased in Canada during the past decade even with the absence of outcomes evidence. Among the 4 most populated provinces in Canada, there is a gradient in the restrictiveness of ezetimibe in the public-funded formularies (most strict to least strict: British Columbia (BC), Alberta (AB), Quebec (QC) and Ontario (ON)). We examined ezetimibe use trends in these 4 provinces in the period before and after the ENHANCE trial, which was published in January 2008 to better understand the association between use and formulary restrictiveness. Methods: We conducted a population-level observational cohort study using the data collected by IMS Health Canada’s CompuScript Audit® from June 1, 2003 to December 31, 2012. The main outcome measure was monthly ezetimibe expenditures, which were used as a proxy for the total number of prescriptions. The differences in ezetimibe-associated expenditures between the 4 provinces were tested using a linear regression model wi...
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".