Ethnic differences in 1-year mortality among patients hospitalised with heart failure
Bibliographic record
Abstract
OBJECTIVE: The incidence of cardiovascular disease and the prevalence of risk factors have been shown to differ significantly across ethnic groups. The objective of this study was to examine the impact of ethnicity on 1-year mortality among patients with heart failure in a single payer healthcare system with universal access. DESIGN, SETTING AND PATIENTS: Alberta residents aged 20 years or older hospitalised with heart failure between 1 April 1999 and 31 December 2005 are included. Previously validated algorithms were used to assign ethnicity based on patient surname. Patients were categorised as white, Chinese or East Indian. MAIN OUTCOME MEASURE: One-year mortality after adjusting for baseline differences. RESULTS: 52 980 white, 851 Chinese, and 377 East Indian individuals were hospitalised with heart failure. Chinese patients were the oldest and had the highest rates of renal disease. East Indian patients were the youngest and had the highest rates of ischaemic heart disease and diabetes. One-year mortality rates were 31.0% among white patients, 38.7% among Chinese and 26.5% among East Indian patients (p<0.01). Adjusted HR (and 95% CI) for 1-year mortality among Chinese compared with white patients was 1.34 (1.20 to 1.49) and among East Indian compared with white patients it was 1.04 (0.85 to 1.27). These findings were consistent across various subgroups, including patients with incident heart failure. CONCLUSIONS: Ethnicity appears to modulate patient outcomes in heart failure. Chinese patients have significantly higher 1-year mortality rates compared with white patients; there appear to be no differences in mortality among East Indian and white patients.
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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 | 0.000 | 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) | 0.002 | 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".