Abstract 153: Impact Of The Enhance Trial On The Use Of Ezetimibe In The Us And Canada
Bibliographic record
Abstract
Background: Clinical evidence, particularly landmark trials, may impact physicians’ prescribing patterns and medication use. Drug policies and marketing may also impact medication use. We examined trends in the use of ezetimibe before and after the reporting of the landmark ENHANCE trial in the United States compared with Canada. Methods: We conducted a population-based, retrospective, time-series analysis using the data collected by IMS Health in the United States and Compuscript in Canada from January 1, 2002 to December 31, 2009. The main outcome measures were monthly number of prescriptions for ezetimibe-containing products before and after the ENHANCE trial. The ENHANCE trial, which was released in mid-January 2008, represented the intervention event. Results: The monthly number of prescriptions for ezetimibe rose from 6 to 1082 per 100,000 population in the United States from November 2002 to January 2008, then started declining to 572 per 100,000 population by December 2009 after the release of the...
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".