Primary Prevention of Cardiovascular Diseases With Statin Therapy
Bibliographic record
Abstract
BACKGROUND: While the role of hydroxymethyl glutaryl coenzyme A reductase inhibitors (statins) in secondary prevention of cardiovascular (CV) events and mortality is established, their value for primary prevention is less clear. To clarify the role of statins for patients without CV disease, we performed a meta-analysis of randomized controlled trials (RCTs). METHODS: MEDLINE, EMBASE, Cochrane Collaboration, and American College of Physicians Journal Club databases were searched for RCTs published between 1966 and June 2005. We included RCTs with follow-up of 1 year or longer, more than 100 major CV events, and 80% or more of the population without CV disease. From each trial, demographic data, lipid profile, CV outcomes, mortality, and adverse outcomes were recorded. Summary relative risk (RR) ratios with 95% confidence intervals (CIs) were calculated using a random effects model. RESULTS: Seven trials with 42,848 patients were included. Ninety percent had no history of CV disease. Mean follow-up was 4.3 years. Statin therapy reduced the RR of major coronary events, major cerebrovascular events, and revascularizations by 29.2% (95% CI, 16.7%-39.8%) (P<.001), 14.4% (95% CI, 2.8%-24.6%) (P = .02), and 33.8% (95% CI, 19.6%-45.5%) (P<.001), respectively. Statins produced a nonsignificant 22.6% RR reduction in coronary heart disease mortality (95% CI, 0.56-1.08) (P = .13). No significant reduction in overall mortality (RR, 0.92 [95% CI, 0.84-1.01]) (P = .09) or increases in cancer or levels of liver enzymes or creatine kinase were observed. CONCLUSION: In patients without CV disease, statin therapy decreases the incidence of major coronary and cerebrovascular events and revascularizations but not coronary heart disease or overall mortality.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".