Study on the impact of the publication of the enhance trial results in a health area of spain
Bibliographic record
Abstract
Background: ENHANCE trial results were published in April 2008, which showed a lack of efficacy of ezetimibe in reducing atherosclerosis. Time series studies found that after the publication of the results of ENHANCE the use of ezetimibe in Canada did not decrease but that in the United States of America the utilization of ezetimibe decreased. Objective: The aim of this study was to measure changes in the quantitative utilization of ezetimibe after the publication of the ENHANCE trial in an area of Spain. Method: Monthly ezetimibe defined daily doses per thousand inhabitants per day (DDD/TID) were calculated between early 2007 and April 2009 in a health area of Spain. Data were analysed graphically and via segmented regression analysis, in order to estimate the impact of the publication of ENHANCE trial results. Results: The graphical representation showed a long term rising trend in the utilization of ezetimibe measured as monthly number of DDD/TID, being such growth at the same pace throughout the whole study period. The relative growth in ezetimibe utilization throughout the whole study period was 60.18%, being the average monthly growth rate of 1.75% per month. Segmented regression analysis showed neither a statistically significant change in the immediate use of ezetimibe after the publication of the ENHANCE clinical trial, nor a statistically significant change in the slope of ezetimibe use after the same clinical trial (long-term impact). Conclusion: The publication of ENHANCE trial did not seem to influence ezetimibe utilization in Spain since its utilization continued to grow at the same rate than previously
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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 | 0.031 | 0.127 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".