Guideline-Directed Low-Density Lipoprotein Management in High-Risk Patients With Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Limited information is available on stroke prevention in high-risk patients with preexisting cardiovascular disease. Our aim was to use admission low-density lipoprotein (LDL) values to evaluate differences in the attainment of National Cholesterol Education Program-Adult Treatment Panel III guidelines goals at the time of the index event in high-risk patients with stroke and preexisting cardio- or cerebrovascular disease. METHODS: Observational study, using data from the Get-With-The-Guidelines-Stroke Registry including 913 436 patients with an acute ischemic stroke or transient ischemic attack from April 2003 to September 2012. Participants were classified as high risk if they had history of transient ischemic attack (TIA), stroke (cardiovascular disease), and coronary artery disease (CAD). RESULTS: Of the 913 436 patients admitted with an acute stroke or TIA, 194 557 (21.3%) had previous stroke/TIA, 148 833 (16.3%) had previous CAD, and 88 605 (9.7%) had concomitant CAD and cardiovascular disease. Overall, only 68% of patients with stroke were at their preadmission National Cholesterol Education Program III guideline-recommended LDL target; 51.3% had LDL <100 mg/dL; and only 19.8% had LDL<70 mg/dL. Among those presenting with a recurrent stroke, >45% had LDL>100 mg/dL. When compared with patients with CAD, patients with previous TIA/stroke were less likely to have LDL<100 or <70 mg/dL. In multivariable analysis, older age, men, white race, lack of major vascular risk factors, previous use of cholesterol-lowering therapy, and care provided in larger hospitals were associated with meeting LDL targets on admission testing. CONCLUSIONS: Management of dyslipidemia in high-risk patients with preexistent CAD or stroke continues to be suboptimal. Only 1 in 5 patients with prior TIA/stroke had LDL levels <70 mg/dL.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".