Effectiveness and tolerability of ezetimibe co-administered with statins versus statin dose-doubling in high-risk patients with persistent hyperlipidemia: The EZE(STAT)2 trial
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: When a standard dose of statins fails to achieve lipid control in patients at high risk for coronary artery disease (CAD), increasing the statin dosage or co-administration of additional agents is recommended. The aim of this study was to compare the safety and lipid-lowering efficacy of doubling the standard statin dose (STAT2) to that of co-administering ezetimibe 10 mg/day (EZE+statin) in Canadian patients at high CAD risk with persistent hyperlipidemia upon statin treatment. MATERIAL AND METHODS: Six-week, open-label, randomized, multicentre study. The primary outcome was the change in plasma LDL-C and secondary measures included the change in additional lipid parameters. Safety was assessed with the incidence of emergent adverse events (AEs). RESULTS: Eight hundred eighty-five patients (EZE+statin, n=586; STAT2, n=299) completed the study. The mean (SD) percent change in low-density lipoprotein cholesterol (LDL-C) was - 30.9% (18.2) for the EZE+statin group and -18.4% (19.0) for the STAT2 group (p=0.001). Percent and absolute decreases in total cholesterol (TC), triglycerides and the TC to high-density lipoprotein cholesterol ratio (TC/HDL-C) were significantly greater for the EZE+statin group (p = 0.001). After 6 weeks of treatment, 70% of the patients in the EZE+statin group and 48% of patients in the STAT2 group (OR=2.45, p<0.001) achieved target LDL-C levels of<2.5 mmol/l. Incidence of AEs was similar between groups, with the exception of a higher incidence of muscle disorders in the STAT2 group. CONCLUSIONS: In patients at high CAD risk who are above the LDL-C target while on statin monotherapy, co-administration of ezetimibe is well tolerated and more effective in improving the lipid profile compared to doubling the existing statin dose.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it