Effects of long term cholesterol lowering on coronary atherosclerosis in patient risk factor subgroups: the Simvastatin/enalapril Coronary Atherosclerosis Trial (SCAT).
Bibliographic record
Abstract
This study examined the effects of long term cholesterol lowering therapy with simvastatin on progression and regression of coronary atherosclerosis, as determined by quantitative angiographic end points, in subgroups of patients with known coronary risk factors. In this randomized, placebo controlled clinical trial, the effect of simvastatin on coronary atherosclerosis was compared with that of placebo in 394 patients who had paired coronary angiograms taken an average of four years apart. The effects of treatment on the following prespecified subgroups were examined: sex, age (less than 65 years versus at least 65 years), smoking status (current or previous/never), history of diabetes mellitus or hypertension, and severity of coronary artery lesions (diameter at least 50% versus less than 50%). There were significantly smaller decreases in the average minimum diameters, between closeout and baseline angiograms, in all simvastatin-treated subgroups, compared with placebo. Trends toward or significantly smaller decreases in the average of the mean diameters, and similar smaller increases in percentage diameter stenosis were also seen in all subgroups. The slowing of angiographically demonstrable coronary atherosclerotic narrowing supports the contention that this treatment effect is causally related to the reduction of coronary events repeatedly seen in large outcome clinical trials of lipid lowering therapy. Also, this treatment effect occurs in the presence or absence of the traditional coronary risk factors.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".