Secondary Prevention of Ischemic Stroke: Evolution from a Stepwise to a Risk Stratification Approach to Care
Bibliographic record
Abstract
Survivors of ischemic stroke are at significant risk for recurrent stroke. Appropriate therapy for stroke prevention is needed given the significant morbidity and mortality associated with stroke, the high financial costs, and the neurologic disability associated with treatment failure. A treatment strategy based on assessed risk represents an appropriate use of medical resources and results in improved outcomes. This approach requires evaluation of major risk factors, the most serious of which is a history of ischemic stroke or transient ischemic attack. The annual risk for recurrent stroke is 6% during the first 5 years after an initial stroke. Non-modifiable risk factors include age, race, ethnicity, gender, family history, and geography. The most important modifiable risk factor is hypertension. Diabetes mellitus, hyperlipidemia, left ventricular hypertrophy, atrial fibrillation, and lifestyle factors such as smoking, alcohol abuse, and obesity contribute to stroke risk. Antihypertensive, lipid-lowering, and antiplatelet therapies have been successful in reducing the incidence of secondary stroke. Clinical trials validate the benefits of statin therapy in reducing the risk for secondary stroke. Studies of antiplatelet agents, including aspirin, clopidogrel, and aspirin combined with extended-release dipyridamole, have evaluated the risk reduction in recurrent stroke and have been concerned particularly with the risk for hemorrhage. Therapy for stroke prevention based on risk stratification can identify patients who are appropriate targets for aggressive intervention.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".