Bibliographic record
Abstract
Statins have become a cornerstone of treatment for dyslipidaemia primarily due to their marked lowering of low-density lipoprotein cholesterol (LDL-C). Studies show that statin treatment typically reduces relative risk of cardiovascular disease by 24-37%, regardless of age, sex, prior history of coronary heart disease (CHD), or other co-morbid conditions. There is also a growing body of evidence that statins can be effective in people whose LDL-C is not considered elevated under current guidelines. In both the Anglo-Scandinavian Cardiac Outcomes Trial (ASCOT) and the Collaborative Atorvastatin Diabetes Study (CARDS), participants randomised to atorvastatin (10 mg/day) experienced at least a one-third reduction in major cardiovascular events, even though at baseline, their LDL-C was within the normal range. Other studies have also provided evidence that more intensive lipid-lowering regimens could provide additional clinical benefits. In the Reversal of Atherosclerosis with Aggressive Lipid Lowering (REVERSAL) trial, the first active-control clinical trial of CHD progression, an intensive lipid-lowering regimen using atorvastatin (80 mg/day) decreased atherogenic lipoproteins and atheroma volume in patients with established CHD, compared with a moderate regimen using pravastatin (40 mg/day). Furthermore, relative to baseline, there was no measurable atheroma progression in the atorvastatin group. While statin therapy does offer significant clinical benefit, 60-70% of major cardiovascular events are still not prevented, which underscores the need for alternative interventions. Targeting inflammatory mediators of atherosclerosis such as C-reactive protein (CRP), as well as combination therapy to simultaneously raise high-density lipoprotein cholesterol (HDL-C) and lower LDL-C, are among the promising new strategies for primary and secondary prevention of atherosclerotic disease. This article will summarise data concerning use of statins in patients without markedly elevated LDL-C. The issue of the ideal LDL-C target will also be considered before addressing future treatment options for dyslipidaemia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".