Potentially modifiable risk factors associated with myocardial infarction in China: the INTERHEART China study
Bibliographic record
Abstract
BACKGROUND: Lifestyle changes associated with the rapidly developing economy increase cardiovascular disease (CVD), myocardial infarction (MI) and cardiovascular risk factors (CVRFs) in China. OBJECTIVE: To assess and compare regionally, and with other regions of the world, distribution of the nine INTERHEART CVRFs, their relationship to MI and the CVD epidemic in China in order to determine how this may influence the future of CVD in China. METHODS: Patients with first acute MI (n = 3030) and age- and sex-matched controls (n = 3056) were enrolled from 26 centres in China. RESULTS: Northern Chinese had higher rates of smoking and hypertension, whereas southern Chinese reported lower fruit and vegetable intake and higher rates of depression. Compared with other regions, participants from China were older, with lower body mass index and waist to hip ratios, lower total and low-density lipoprotein cholesterol levels, ApoB lipoprotein and ApoB to ApoA-1 ratios, but higher high-density lipoprotein cholesterol and ApoA-1. All nine INTERHEART CVRFs, education and income were significantly associated with MI in the Chinese cohort. There was significant heterogeneity in the strength of association between certain CVRFs and MI for China versus other regions, with stronger associations for the Chinese for diabetes (OR 5.10 vs 2.84), depression (2.27 vs 1.37) and permanent stress (2.67 vs 2.06); and lower for the Chinese for abdominal obesity (1.33 vs 2.62) (p for heterogeneity, all <0.001). CONCLUSIONS: Diabetes and psychosocial factors have strong associations with risk of MI in China, indicating that future increases in these risk factors with societal change in China may hasten rapid increases in CVD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".