Immigration Status, Ethnicity, and Long-term Outcomes Following Ischemic Stroke
Bibliographic record
Abstract
Objective To assess the association between immigration status and ethnicity and the outcomes of mortality and vascular event recurrence following ischemic stroke in Ontario, Canada. Methods We conducted a retrospective cohort study using linked administrative and clinical registry–based data from 2002 to 2018 and compared hazards of all-cause mortality and vascular event recurrence in immigrants and long-term residents using inverse probability of treatment weighting accounting for age, sex, income, and comorbidities. We stratified analyses by age (≤75 and >75 years) and used interaction terms to evaluate whether the association between immigration status and outcomes varied with age or ethnicity. Results We followed 31,918 adult patients, of whom 2,740 (8.6%) were immigrants, for a median follow-up of 5 years. Immigrants had lower mortality than long-term residents (46.1% vs 64.5%), which was attenuated after adjustment (hazard ratio [HR] 0.94; 95% confidence interval [CI] 0.88–1.00), but persisted in those younger than 75 years (HR 0.82; 0.74–0.91). Compared to their respective ethnic long-term resident counterparts, the adjusted hazard of death was higher in South Asian immigrants, similar in Chinese immigrants, and lower in other immigrants (p value for interaction = 0.003). The adjusted hazard of vascular event recurrence (HR 1.01; 0.92–1.11) was similar in immigrants and long-term residents, and this observation persisted across all age and ethnic groups. Conclusions Long-term mortality following ischemic stroke is lower in immigrants than in long-term residents, but is similar after adjustment for baseline characteristics, and it is modified by age at the time of stroke and by ethnicity.
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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 | 0.000 | 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".