Marital Status, Education, and Risk of Acute Myocardial Infarction in Mainland China: The INTER-HEART Study
Bibliographic record
Abstract
BACKGROUND: We investigated the effects of marital status and education on the risk of acute myocardial infarction (AMI) in a large-scale case-control study in China. METHODS: This study was part of the INTER-HEART China case-control study. The main outcome measure was first AMI. Incident cases of AMI and control patients with no past history of heart disease were recruited. Controls were matching by age (±5 years) and sex. Marital status was combined into 2 categories: single and not single. Education level was classified into 2 categories: 8 years or less and more than 8 years. RESULTS: From 1999 to 2002, we recruited 2909 cases and 2947 controls from 17 cities. After adjustment for age, sex, BMI, psychosocial factors, lifestyle, other factors, and mutually for other risk factors, the odds ratio (OR) for AMI associated with being single was 1.51 (95% confidence interval: 1.18-1.93) overall, 1.19 (0.84-1.68; P = 0.072) in men and 2.00 (1.39-2.86; P < 0.0001) in women. The interaction of sex and marital status was statistically significant (P = 0.045). Compared with a high education level, a low education level increased the risk of AMI (1.45, 1.26-1.67); the odds ratios in men and women were 1.29 (1.09-1.52) and 1.55 (1.16-2.08), respectively. Single women with a low education level had a high risk of AMI (2.95, 1.99-4.37). CONCLUSIONS: Being single was consistently associated with an increased risk for AMI, particularly in women. In addition, as compared with high education level, low education level was associated with a higher risk of AMI in both men and women.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".