More Than 50 Percent Reduction in LDL Cholesterol in Patients With Target LDL <70 mg/dL After a Stroke
Bibliographic record
Abstract
BACKGROUND: Whether a strategy to target an LDL (low-density lipoprotein) cholesterol <70 mg/dL is more effective when LDL is reduced >50% from baseline rather than <50% from baseline has not been investigated. METHODS: The Treat Stroke to Target trial was conducted in France and South Korea in 61 sites between March 2010 and December 2018. Patients with ischemic stroke in the previous 3 months or transient ischemic attack within the previous 15 days and evidence of cerebrovascular or coronary artery atherosclerosis were randomly assigned to a target LDL cholesterol of <70 mg/dL or 100±10 mg/dL, using statin and/or ezetimibe as needed. We used the results of repeated LDL measurements (median, 5 [2-6] per patient) during 3.9 years (interquartile range, 2.1-6.8) of follow-up. The primary outcome was the composite of ischemic stroke, myocardial infarction, new symptoms requiring urgent coronary or carotid revascularization, and vascular death. Cox regression model including lipid-lowering therapy as a time-varying variable, after adjustment for randomization strategy, age, sex, index event (stroke or transient ischemic attack), and time since the index event. RESULTS: =0.75). CONCLUSIONS: In this post hoc analysis of the TST trial, targeting an LDL cholesterol of <70 mg/dL reduced the risk of primary outcome compared with 100±10 mg/dL provided LDL cholesterol reduction from baseline was superior to 50%, thereby suggesting that the magnitude of LDL cholesterol reduction was as important to consider as the target level to achieve. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT01252875. URL: https://clinicaltrialsregister.eu; Unique identifier: EUDRACT2009-A01280-57.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".