Cholesterol lowering for secondary prevention: what statin dose should we use?
Bibliographic record
Abstract
Over the past decade, 17 large placebo-controlled trials have established that statin therapy lowers LDL cholesterol and prevents cardiovascular events and death in patients with coronary disease or at high risk for atherosclerotic events. Nine trials of higher dose vs. lower dose statins (reporting data from 29,853 patients with coronary artery disease and 486 patients with other indications for statin therapy) have established that higher dose statin therapy is more efficacious than lower dose therapy in reducing myocardial infarctions/coronary death (by 16%) and stroke (by 18%) in patients with coronary disease but only reduces all-cause mortality in patients at high risk for coronary death (such as patients immediately after acute coronary syndrome). Higher dose statins are associated with statistically significantly increased risks of myopathy and elevated transaminases compared to lower dose statins; while relative risks for these outcomes are 1.2 and 4.0, the absolute increases are small (0.5% and 1%). Secondary analyses of these trials using individual patient data and multivariate adjustment will be needed to appropriately examine the incremental benefits of different LDL targets, and trials are needed to determine whether combinations of low dose statins plus other lipid lowering agents may achieve better clinical outcomes than higher dose statin therapy alone.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".