Influence of education and income on atherogenic risk factor profiles among patients hospitalized with acute myocardial infarction.
Bibliographic record
Abstract
BACKGROUND: Survival after acute myocardial infarction (AMI) varies with socioeconomic status. It is unknown whether these differences can be attributed, in part, to variations in the prevalence of atherogenic risk factors preceding the index AMI event. OBJECTIVES: To examine how cardiovascular risk factors varied according to person-level indicators of income and education among a cohort of younger patients (younger than 65 years of age) hospitalized with AMI in Ontario. METHODS: The Socio-Economic and Acute Myocardial Infarction study (SESAMI) prospectively assembled a cohort of 3335 patients hospitalized with AMI who consented to participate (75% consent rate) from 53 of 57 large-volume institutions (100 AMI cases per year or more) throughout Ontario between December 1, 1999, and June 1, 2002. Given the known challenges inherent in characterizing the socioeconomic status in elderly patients and the ubiquity of atherosclerosis in elderly persons, the study focused on 1635 nonelderly participants. The relationship between income or education and cardiovascular risk factors, after adjustment for age, sex, ethnoracial factors and geography (urban-rural status) was examined. RESULTS: The prevalence of diabetes, hypertension, smoking and pre-existing heart disease was higher among poorer, less educated patients, as were the total number of cardiovascular risk factors. After adjusting for baseline factors, both income (adjusted OR 0.50, 95% CI 0.31 to 0.82, P=0.006) and education (adjusted OR 0.52, 95% CI 0.31 to 0.87, P=0.01) were independently associated with cardiovascular risk factors or pre-existing heart disease. There were no significant interactions between income, education and baseline cardiovascular risk. CONCLUSIONS: Outcome differences across socioeconomic strata following AMI may reflect major income- and education-related differences in atherogenic risk profile.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".