The Risk Factors of Premature Myocardial Infarction: A Case Control Study in Iran
Bibliographic record
Abstract
BACKGROUND: Although, our knowledge about the factors of cardio vascular diseases (CVD) is relatively acceptable, we have few publications about the risk factors of premature CVD particularly from developing countries such as Iran. OBJECTIVES: The present study is aimed to assess the risk of the modifiable risk factors of coronary heart disease (CHD) in the first cardiac attack of patients classified by age to check which risk factors might have a more significant impact to increase the risk of premature myocardial infarction (MI). METHODS: In a case control study, 122 and 266 cases and controls were recruited from one of the main referral centres in Tehran. Cases were those who were hospitalized because of their first myocardial infarction before the ages of 50 and 55 years in males and females respectively, and compared their risk factor profiles with those experienced first MI in higher age. Main independent variables in this study were: the demographic variables, distress, lipid profile, diabetes, smoking, family history of cardio vascular diseases, and physical activity. RESULTS: The results showed that distress (OR= 3.95), minorities (compare to Fars race) (OR= 3.30), higher education (OR= 1.30), family history of hyperlipidaemia (OR=1.89) significantly increased the risk of premature MI. We also found that family history of hypertension (OR=1.35), current smoking versus no smoking (OR=1.60), fast-food consumption (OR=1.48), non-alcoholic beverages (OR= 1.12) had also association with the risk of premature MI but only in crude model. We also found that regular physical activity (OR=0.42), ex-smoker versus current smoking (OR=0.27) and regular consumption of milk (OR=0.73) was protective against premature MI. CONCLUSION: Our findings demonstrate that the effect of most of risk factors were mostly related to their life-style (distress, smoking, physical inactivity and dietary pattern); while family history of diseases was also important.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".