Association between education and blood lipid levels as income increases over a decade: a cohort study
Bibliographic record
Abstract
Cardiovascular risk factors have increased along with economic development, but it is not clear if this tendency differs by education. The aim of this study was to analyze the effect of education on blood lipid levels while income increases over a decade in Chilean adults. A cohort study was conducted from 3092 births in Limache Hospital between 1974 and 1978, of which 998 people were randomly selected in 2000 and 650 followed up in 2010. Using mediation analysis, the controlled direct effect (CDE) of education in 2000 on blood lipid levels in 2010: triglycerides (TG), total cholesterol (TC), LDL cholesterol (LDL) and HDL cholesterol (HDL) while setting the mediator, income, to “increased” between 2000 and 2010 was estimated. The results were expressed through the CDE and its 95% confidence interval (CI). Of the 650 adults, 24% had low education (≤ 8 years) and 60% increased their income. The mediation analysis showed that, when setting income to “increased”, women with low education had worse lipid profiles than women with high education: TG CDE = 14 (CI = − 7;34), TC CDE = 4 (CI = − 8;15), LDL CDE = 1 (CI = − 8;9), HDL CDE = − 3 (CI = − 7;0), while men with low education had better lipid profiles than men with high education: TG CDE = − 2 (CI = − 41;38), TC CDE = − 12 (CI = − 29;5), LDL CDE = − 12 (CI = − 24;1), HDL CDE = 1 (CI = − 5;6). Faced with a rise in income, there was a trend to associate low education with worse lipid profiles in women and better lipid profiles in men.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".