Lipid‐altering efficacy and safety profile of combination therapy with ezetimibe/statin vs. statin monotherapy in patients with and without diabetes: an analysis of pooled data from 27 clinical trials
Bibliographic record
Abstract
AIM: This post hoc analysis compared the lipid-altering efficacy and safety of ezetimibe 10 mg plus statin (EZE/statin) vs. statin monotherapy in hypercholesterolaemic patients with and without diabetes. METHODS: A pooled analysis of 27 previously published, randomized, double-blind, active- or placebo-controlled clinical trials comprising 21 794 adult patients with (n = 6541) and without (n = 15253) diabetes receiving EZE/statin or statin alone for 4-24 weeks evaluated percentage change from baseline in lipids and other parameters. Consistency of the treatment effect across the subgroups was tested using treatment × subgroup interaction. No multiplicity adjustments were made. RESULTS: Treatment effects within both subgroups were generally consistent with the overall population. EZE/statin was more effective than statin alone in improving low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), triglycerides (TGs), non-HDL-C, apolipoprotein (apo) B and high-sensitivity C-reactive protein (hs-CRP) in the overall population and both subgroups. Patients with diabetes achieved significantly larger reductions in LDL-C, TC and non-HDL-C compared with non-diabetic patients. Incidences of adverse events or creatine kinase elevations were similar between groups. A small but significantly higher incidence of alanine aminotransferase or aspartate aminotransferase elevations was seen in patients receiving EZE/statin (0.6%) vs. statin monotherapy (0.3%) in the overall population. CONCLUSIONS: Treatment with EZE/statin vs. statin monotherapy provided significantly larger reductions in LDL-C, TC, TG, non-HDL-C, apo B and hs-CRP and significantly greater increases in HDL-C, with a similar safety profile in patients with and without diabetes. Reductions in LDL-C, TC and non-HDL-C were larger in patients with diabetes than in patients without diabetes.
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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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.017 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".