A comparison of Chinese and non-Chinese Canadian patients hospitalized with heart failure
Bibliographic record
Abstract
BACKGROUND: Canadians of Chinese descent, represent one of the fastest growing visible minority groups in Canada, (as well as the second largest), but relatively little is known about the clinical features of heart failure (HF) in Chinese-Canadian versus non-Chinese Canadian patients. METHODS: We conducted a population-based analysis of urban patients hospitalized in Ontario, Canada for the first time with a most responsible diagnosis of HF between April 1, 1995 and March 31, 2008. Among the 99,278 patients, 1,339 (1.3%) were classified as Chinese using a previously validated list of Chinese surnames. Through linkage to other administrative databases, we compared the clinical characteristics, pharmacological management, and outcomes of Chinese versus non-Chinese HF patients. RESULTS: Ischemic heart disease was identified as the possible etiology of HF in a greater proportion of non-Chinese patients (47.7% vs. 35.3%; p < 0.001) whereas hypertension (26.1% vs. 16.1%; p < 0.001) and valvular heart disease (11.6% vs. 7.2%; p < 0.001) were relatively more common in Chinese patients. Chinese patients were prescribed angiotensin-converting enzyme (ACE) inhibitors less frequently (57.5% vs. 66.4%, p < 0.001) and angiotensin receptor blockers (ARBs) more frequently (17.4% vs. 8.9%, p < 0.001) compared to non-Chinese patients. They were also less likely to be adherent to ACE inhibitors over a 1-year follow up period. However, the 1-year case-fatality rates were comparable between the Chinese (31.7%) and non-Chinese (30.2%) subjects (p = 0.24). CONCLUSION: There are important differences in the causes and medical management of HF in Chinese and non-Chinese patients residing in Canada. Despite these differences, the long-term outcomes of HF patients were similar.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".