Effectiveness of Ezetimibe in Reducing the Estimated Risk for Fatal Cardiovascular Events in Hypercholesterolaemic Patients with Inadequate Lipid Control While on Statin Monotherapy as Measured by the SCORE Model
Bibliographic record
Abstract
Objectives. The aim of this prospective cohort, multicentre study was to assess the effect of coadministrating ezetimibe 10 mg/day with an ongoing statin on the estimated risk for Cardiovascular (CVD) mortality in patients with persistently elevated LDL-C after statin monotherapy. Methods. The Systematic Coronary Risk Evaluation (SCORE) function was used to estimate the 10-year risk for cardiovascular mortality at baseline and 6 weeks. Primary outcome measures were absolute and percent changes in estimated Coronary Heart Disease (CHD) Mortality Risk, and general CVD Mortality Risk (Total CVD Mortality Risk). Results. 825 patients were included in the analysis. Mean (SD) age was 62 (10.5) years and 62.3% were males. The mean (SD) estimated Total CVD Mortality Risk decreased from 0.068 (0.059) at baseline to 0.053 (0.046) at 6 weeks (RR = 0.77; 95% CI:0.689-0.867), while the estimated CHD Mortality Risk decreased from 0.047 (0.040) at baseline to 0.034 (0.029) at 6 weeks (RR = 0.72; 95% CI:0.624-0.826). Conclusions. Co-administration of ezetimibe with a statin is effective in significantly reducing the estimated risk for cardiovascular mortality as measured by the SCORE model.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".