Combining Multiple Approaches for the Secondary Prevention of Vascular Events After Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Numerous effective strategies for the secondary prevention of cardiovascular events in high-risk patients have now been established. We sought to calculate the cumulative benefit of combining multiple strategies for preventing recurrent events in patients with a history of ischemic stroke or transient ischemic attack. METHODS: A comprehensive literature search was undertaken to identify meta-analyses of randomized controlled trials reporting on the efficacy of secondary prevention strategies. The baseline incidence of vascular events was modeled from the Life Long After Cerebral Ischemia study. Strategies were combined on a multiplicative scale and cumulative risk reductions were computed over a 5-year interval. RESULTS: The combination of 5 proven strategies applied to survivors of an initial stroke or transient ischemic attack--dietary modification, exercise, aspirin, a statin, and an antihypertensive agent--could result in a cumulative relative risk reduction of 80%. Given a 5-year major cardiovascular event rate of 24%, this translates to a number needed to treat of about 5. Further gains would result from applying multimodality therapy over longer intervals and enriching the base strategy with dual antiplatelet therapy, high-dose statins, and more intensive blood pressure-lowering. Even more benefit would be present in high-risk subgroups with the addition, where appropriate, of carotid endarterectomy, moderate intensity oral anticoagulants, glycemic control, and smoking cessation. CONCLUSIONS: At least four-fifths of recurrent vascular events in patients with cerebrovascular disease might be prevented by application of a comprehensive, multifactorial approach.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".