Life-Course Socioeconomic Position and Incidence of Coronary Heart Disease
Bibliographic record
Abstract
Cumulative exposure to socioeconomic disadvantage across the life course may be inversely associated with coronary heart disease (CHD); the mechanisms are not fully clear. An objective of this study was to determine whether cumulative life-course socioeconomic position (SEP) is associated with CHD incidence in a well-characterized US cohort that had directly assessed childhood and adulthood measures of SEP and prospectively measured CHD incidence. Furthermore, analyses aimed to evaluate whether adjustment for CHD risk factors reduces the association between cumulative life-course SEP and CHD. The authors examined 1,835 subjects who participated in the Framingham Heart Study Offspring Cohort from 1971 through 2003 (mean age, 35.0 years; 52.4% women). Childhood SEP was measured as father's education; adulthood SEP was assessed as own education and occupation. CHD incidence included myocardial infarction, coronary insufficiency, and coronary death. Cox proportional hazards analyses indicated that cumulative SEP was associated with incident CHD after adjustment for age and sex (hazard ratio = 1.82, 95% confidence interval: 1.17, 2.85 for low vs. high cumulative SEP score). Adjustment for CHD risk factors reduced that magnitude of association (hazard ratio = 1.29, 95% confidence interval: 0.78, 2.13). These findings underscore the potential importance of CHD prevention and treatment efforts for those whose backgrounds include low SEP throughout life.
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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 | 0.000 |
| 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".