Secular Trends in Ischemic Stroke Subtypes and Stroke Risk Factors
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Early diagnosis and treatment of a stroke improves patient outcomes, and knowledge of the cause of the initial event is crucial to identification of the appropriate therapy to maximally reduce risk of recurrence. Assumptions based on historical frequency of ischemic subtypes may need revision if stroke subtypes are changing as a result of recent changes in therapy, such as increased use of statins. METHODS: We analyzed secular trends in stroke risk factors and ischemic stroke subtypes among patients with transient ischemic attack or minor or moderate stroke referred to an urgent transient ischemic attack clinic from 2002 to 2012. RESULTS: There was a significant decline in low-density lipoprotein cholesterol and blood pressure, associated with a significant decline in large artery stroke and small vessel stroke. The proportion of cardioembolic stroke increased from 26% in 2002 to 56% in 2012 (P<0.05 for trend). Trends remained significant after adjusting for population change. CONCLUSIONS: With more intensive medical management in the community, a significant decrease in atherosclerotic risk factors was observed, with a significant decline in stroke/transient ischemic attack caused by large artery atherosclerosis and small vessel disease. As a result, cardioembolic stroke/transient ischemic attack has increased significantly. Our findings suggest that more intensive investigation for cardiac sources of embolism and greater use of anticoagulation may be warranted.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".