Relative and Cumulative Effects of Lipid and Blood Pressure Control in the Stroke Prevention by Aggressive Reduction in Cholesterol Levels Trial
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The relative contributions of on-treatment low- and high-density lipoprotein cholesterol (LDL-C, HDL-C), triglycerides, and blood pressure (BP) control on the risk of recurrent stroke or major cardiovascular events in patients with stroke is not well defined. METHODS: We randomized 4731 patients with recent stroke or transient ischemic attack and no known coronary heart disease to atorvastatin 80 mg per day or placebo. RESULTS: After 4.9 years, at each level of LDL-C reduction, subjects with HDL-C value above the median or systolic BP below the median had greater reductions in stroke and major cardiovascular events and those with a reduction in triglycerides above the median or diastolic BP below the median showed similar trends. There were no statistical interactions between on-treatment LDL-C, HDL-C, triglycerides, and BP values. In a further exploratory analysis, optimal control was defined as LDL-C <70 mg per deciliter, HDL-C >50 mg per deciliter, triglycerides <150 mg per deciliter, and SBP/DBP <120/80 mm Hg. The risk of stroke decreased with as the level of control increased (hazard ratio [95% confidence interval] 0.98 [0.76 to 1.27], 0.78 [0.61 to 0.99], 0.62 [0.46 to 0.84], and 0.35 [0.13 to 0.96]) for those achieving optimal control of 1, 2, 3, or 4 factors as compared to none, respectively. Results were similar for major cardiovascular events. CONCLUSIONS: We found a cumulative effect of achieving optimal levels of LDL-C, HDL-C, triglycerides, and BP on the risk of recurrent stroke and major cardiovascular events. The protective effect of having a higher HDL-C was maintained at low levels of LDL-C.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".