Outcomes in a diabetic population of south Asians and whites following hospitalization for acute myocardial infarction: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to determine whether South Asian patients with diabetes have a worse prognosis following hospitalization for acute myocardial infarction (AMI) compared with their White counterparts. We measured the risk of developing a composite cardiovascular outcome of recurrent AMI, congestive heart failure (CHF) requiring hospitalization, or death, in these two groups. METHODS: Using hospital administrative data, we performed a retrospective cohort study of 41,615 patients with an incident AMI in British Columbia and the Calgary Health Region between April 1, 1995, and March 31, 2002. South Asian ethnicity was determined using validated surname analysis. Baseline demographic characteristics and co-morbidities were included in Cox proportional hazard models to compare time to reaching the composite outcome and its individual components. RESULTS: Among the AMI cohort, 29.7% of South Asian patients and 17.6% of White patients were identified as having diabetes (n = 7416). There was no significant difference in risk of developing the composite cardiovascular outcome (Hazard Ratio = 0.90, 95% CI = 0.80-1.01). However, South Asian patients had significantly lower mortality at long term follow-up (HR = 0.62, 95% CI = 0.51-0.74) compared to their White counterparts. CONCLUSIONS: Following hospitalization for AMI, South Asian patients with diabetes do not have a significantly different long term risk of a composite cardiovascular outcome compared to White patients with diabetes. While previous research has suggested worse cardiovascular outcomes in the South Asian population, we found lower long-term mortality among South Asians with diabetes following AMI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".