Abstract WP193: Secular Trends in Ischemic Stroke Subtypes
Bibliographic record
Abstract
Background: In the past it was thought that ~ 20-25% of ischemic strokes were cardioembolic, ~ 20% from large artery disease and ~ 20% from small vessel disease. With the aging of the population, and with increasing prevalence of therapy for hypertension and hyperlipidemia, it might be expected that stroke subtypes would be changing over time. Purpose and Methods: We studied the change over time of ischemic stroke subtypes from 2000-2012, among 2300 patients referred to the Urgent TIA Clinic at University Hospital, London, Canada. We divided the patients into 3 eras that were approximately equal in numbers: Era 1 (2000-2004), Era 2 (2005-2007) and Era 3 (since 2008). Ischemic stroke subtypes were defined by clinical and imaging criteria into evident, probable and possible causes. Results: Mean age + SD was 63.39 + 17.47 years; 50% were women, 17.7% were diabetic, 19.6% current smokers. The proportion of patients with known atrial fibrillation increased from 4.2% in Era 1 to 6.6% in Era 2, to 9.6% in Era 3 (p-0.001). Among patients thought to have cardioembolic stroke, atrial fibrillation accounted for 95.8% in Era 1, 93.4% in Era 2 and 90.4% in Era 3 (p=0.001). Among patients whose stroke subtype was regarded as evident or probable (n=1699), stroke subtypes changed over time, as shown in Table 1: Similar trends were observed If all patients (evident, probable, possible) were included. Stroke subtypes have changed significantly since 2000 (Chi-Square 2p=0.0001), with a marked increase in cardioembolic stroke, and a decline in large artery stroke. Conclusion: In recent years, the proportion of patients with cardioembolic stroke has increased significantly. This has important implications for treatment to reduce the risk of recurrent stroke
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".