Abstract TP176: Stroke Recurrence Among South Asians With diabetes in Ontario, Canada
Bibliographic record
Abstract
Introduction: South Asians have earlier cardiovascular disease onset and higher stroke mortality than non-South Asians, but disparities in long-term outcomes after stroke in patients with diabetes are not well described. We compared risk of cardiovascular outcomes after first ischemic stroke between South Asian and non-South Asian patients with diabetes. Hypothesis: We hypothesized that South Asian stroke patients with diabetes were younger and had higher stroke recurrence, even accounting for competing risk of death. Methods: Using population-based health care databases in Ontario, Canada, we selected all patients with diabetes hospitalized with first ischemic stroke between April 1, 2002 and March 31, 2012, and assigned South Asian vs. non-South Asian ethnicity using a validated surname algorithm. Kaplan-Meier survival analysis and competing risk models estimated risk of death, stroke, and myocardial infarction. The primary predictor was ethnicity; models were adjusted for demographics and vascular risk factors. Sensitivity analysis including adjustment for medication use was performed in those aged >=65 years. Results: There were 25495 diabetics with first ischemic stroke; 840 were South Asian and were younger, more often male, had lower income, and had shorter Ontario residency compared to non-South Asians. South Asians had higher incidence and cumulative risk of recurrent stroke (Figure). In adjusted competing risk models, recurrent stroke risk was increased among South Asians compared to non-South Asians (HR 1.17 [95% CI 1.00-1.38]) in the whole cohort and in those aged >=65 years, both with adjustment for medication use (HR 1.23 [1.01-1.50]) and without (1.27 [1.04-1.54]). Conclusions: In this large population-based study, South Asian stroke patients with diabetes had higher recurrent stroke risk compared to non-South Asians, despite younger age. Further research and targeted interventions are needed to reduce stroke burden in South Asians.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".