Outcomes After Acute Myocardial Infarction in South Asian, Chinese, and White Patients
Bibliographic record
Abstract
BACKGROUND: Cardiac mortality rates vary substantially between countries and ethnic groups. It is unclear, however, whether South Asian, Chinese, and white populations have a variable prognosis after acute myocardial infarction (AMI). To clarify this association, we compared mortality, use of revascularization procedures, and risk of recurrent AMI and hospitalization for heart failure between these ethnic groups in a universal-access healthcare system. METHODS AND RESULTS: We used a population cohort study design using hospital administrative data linked to cardiac procedure registries from British Columbia and the Calgary Health Region Area in Alberta (1994 to 2003) to identify AMI cases. Patient ethnicity was categorized using validated surname algorithms. There were 2190 South Asian, 946 Chinese, and 38479 white patients with AMI identified. There was no significant difference in use of revascularization procedures between ethnic groups at 30 d and 1 year. Short-term (30-day) mortality was higher among Chinese relative to white patients (odds ratio, 1.23; 95% confidence interval, 1.02 to 1.48). There was no significant difference in 30-day mortality between South Asian and white patients. South Asian patients had a 35% lower relative risk of long-term mortality compared with white patients (hazard ratio, 0.65; 95% confidence interval, 0.57 to 0.72). There was no significant difference in long-term mortality between Chinese and white patients. Among AMI survivors, Chinese patients had a lower risk of recurrent AMI, whereas there was no difference between South Asian and white patients. CONCLUSION: The ethnic groups studied have striking differences in outcomes after AMI, with South Asian patients having significantly lower long-term mortality after AMI.
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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 | 0.001 |
| 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".