Risk factors, quality of care and prognosis in South Asian, East Asian and White patients with stroke
Bibliographic record
Abstract
BACKGROUND: Stroke has emerged as a significant and escalating health problem for Asian populations. We compared risk factors, quality of care and risk of death or recurrent stroke in South Asian, East Asian and White patients with acute ischemic and hemorrhagic stroke. METHODS: Retrospective analysis was performed on consecutive patients with ischemic stroke or intracerebral hemorrhage admitted to 12 stroke centers in Ontario, Canada (July 2003-March 2008) and included in the Registry of the Canadian Stroke Network database. The database was linked to population-based administrative databases to determine one-year risk of death or recurrent stroke. RESULTS: The study included 253 South Asian, 513 East Asian and 8231 White patients. East Asian patients were more likely to present with intracerebral hemorrhage (30%) compared to South Asian (17%) or White patients (15%) (p<0.001). Time from stroke to hospital arrival was similarly poor with delays >2 hours for more than two thirds of patients in all ethnic groups. Processes of stroke care, including thrombolysis, diagnostic imaging, antithrombotic medications, and rehabilitation services were similar among ethnic groups. Risk of death or recurrent stroke at one year after ischemic stroke was similar for patients who were White (27.6%), East Asian (24.7%, aHR 0.97, 95% CI 0.78-1.21 vs. White), or South Asian (21.9%, aHR 0.91, 95% CI 0.67-1.24 vs. White). Although risk of death or recurrent stroke at one year after intracerebral hemorrhage was higher in East Asian (35.5%) and White patients (47.9%) compared to South Asian patients (30.2%) (p=0.002), these differences disappeared after adjustment for age, sex, stroke severity and comorbid conditions (aHR 0.89 [0.67-1.19] for East Asian vs White and 0.99 [0.54-1.81] for South Asian vs. White). CONCLUSION: After stratification by stroke type, stroke care and outcomes are similar across ethnic groups in Ontario. Enhanced health promotion is needed to reduce delays to hospital for all ethnic groups.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".