Risk Factors for Acute Myocardial Infarction in Latin America
Bibliographic record
Abstract
BACKGROUND: Current knowledge of the impact of cardiovascular risk factors in Latin America is limited. METHODS AND RESULTS: As part of the INTERHEART study, 1237 cases of first acute myocardial infarction and 1888 age-, sex-, and center-matched controls were enrolled from Argentina, Brazil, Colombia, Chile, Guatemala, and Mexico. History of smoking, hypertension, diabetes mellitus, diet, physical activity, alcohol consumption, psychosocial factors, anthropometry, and blood pressure were recorded. Nonfasting blood samples were analyzed for apolipoproteins A-1 and B-100. Logistic regression was used to estimate multivariate adjusted odds ratios (ORs) and their 95% confidence intervals (CIs). Persistent psychosocial stress (OR, 2.81; 95% CI, 2.07 to 3.82), history of hypertension (OR, 2.81; 95% CI, 2.39 to 3.31), diabetes mellitus (OR, 2.59; 95% CI, 2.09 to 3.22), current smoking (OR, 2.31; 95% CI, 1.97 to 2.71), increased waist-to-hip ratio (OR for first versus third tertile, 2.49; 95% CI, 1.97 to 3.14), and increased ratio of apolipoprotein B to A-1 (OR for first versus third tertile, 2.31; 95% CI, 1.83 to 2.94) were associated with higher risk of acute myocardial infarction. Daily consumption of fruits or vegetables (OR, 0.63; 95% CI, 0.51 to 0.78) and regular exercise (OR, 0.67; 95% CI, 0.55 to 0.82) reduced the risk of acute myocardial infarction. Abdominal obesity, abnormal lipids, and smoking were associated with high population-attributable risks of 48.5%, 40.8%, and 38.4%, respectively. Collectively, these risk factors accounted for 88% of the population-attributable risk. CONCLUSIONS: Interventions aimed at decreasing behavioral risk factors, lowering blood pressure, and modifying lipids could have a large impact on the risk of acute myocardial infarction among Latin Americans.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".