Rationale and design of <scp>REDUCE‐IT</scp>: Reduction of Cardiovascular Events with Icosapent Ethyl–Intervention Trial
Bibliographic record
Abstract
Residual cardiovascular risk persists despite statins, yet outcome studies of lipid-targeted therapies beyond low-density lipoprotein cholesterol (LDL-C) have not demonstrated added benefit. Triglyceride elevation is an independent risk factor for cardiovascular events. High-dose eicosapentaenoic acid (EPA) reduces triglyceride-rich lipoproteins without raising LDL-C. Omega-3s have postulated pleiotropic cardioprotective benefits beyond triglyceride-lowering. To date, no large, multinational, randomized clinical trial has proved that lowering triglycerides on top of statin therapy improves cardiovascular outcomes. The Reduction of Cardiovascular Events with Icosapent Ethyl-Intervention Trial (REDUCE-IT; NCT01492361) is a phase 3b randomized, double-blinded, placebo-controlled trial of icosapent ethyl, a highly purified ethyl ester of EPA, vs placebo. The main objective is to evaluate whether treatment with icosapent ethyl reduces ischemic events in statin-treated patients with high triglycerides at elevated cardiovascular risk. REDUCE-IT enrolled men or women age ≥45 years with established cardiovascular disease or age ≥50 years with diabetes mellitus and 1 additional risk factor. Randomization required fasting triglycerides ≥150 mg/dL and <500 mg/dL and LDL-C >40 mg/dL and ≤100 mg/dL with stable statin (± ezetimibe) ≥4 weeks prior to qualifying measurements. The primary endpoint is a composite of cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, coronary revascularization, or unstable angina. The key secondary endpoint is the composite of cardiovascular death, nonfatal myocardial infarction, or nonfatal stroke. Several secondary, tertiary, and exploratory endpoints will be assessed. Approximately 8000 patients have been randomized at approximately 470 centers worldwide. Follow-up will continue in this event-driven trial until approximately 1612 adjudicated primary-efficacy endpoint events have occurred.
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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 | 0.037 | 0.024 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.024 | 0.013 |
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".