The comparative efficacy of ezetimibe added to atorvastatin 10 mg versus uptitration to atorvastatin 40 mg in subgroups of patients aged 65 to 74 years or greater than or equal to 75 years
Bibliographic record
Abstract
BACKGROUND: Coronary heart disease (CHD) risk increases with age; yet lipid-lowering therapies are significantly under-utilized in patients > 65 years. The objective was to evaluate the safety and efficacy of lipid-lowering therapies in older patients treated with atorvastatin 10 mg + ezetimibe 10 mg (EZ/Atorva) vs. increasing the atorvastatin dose to 40 mg. METHODS: Patients ≥ 65 years with atherosclerotic vascular disease (LDL-C ≥ 1.81 mmol/L) or at high risk for coronary heart disease (LDL-C ≥ 2.59 mmol/L) were randomized to EZ/Atorva for 12 wk vs. uptitration to atorvastatin 20 mg for 6 wk followed by atorvastatin 40 mg for 6 wk. The percent change in LDL-C and other lipid parameters and percent patients achieving prespecified LDL-C levels were assessed after 12 wk. RESULTS: EZ/Atorva produced greater reductions in most lipid parameters vs. uptitration of atorvastatin in patients ≥ 75 years (n = 228), generally consistent with patients 65-74 years (n = 812). More patients achieved LDL-C targets with combination therapy vs. monotherapy in both age groups at 6 wk and in patients ≥ 75 years at 12 wk. At 12 wk, more patients ≥ 75 years achieved LDL-C targets with monotherapy vs. combination therapy. EZ/Atorva produced more favorable improvements in most lipids vs. doubling or quadrupling the atorvastatin dose in patients ≥ 75 years, generally consistent with the findings in patients 65-74 years. CONCLUSIONS: Our results extended previous findings demonstrating that ezetimibe added to a statin provided a generally well-tolerated therapeutic option for improving the lipid profile in patients 65 to 74 years and ≥ 75 years of age.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".