Racial differences of lipoprotein subclass distributions in postmenopausal women.
Bibliographic record
Abstract
BACKGROUND: We assessed racial differences in lipoprotein particle size, a marker of atherosclerosis risk, among women with coronary disease. METHODS: We studied 378 women (33% non-White, predominantly African American) at the baseline visit of the Women's Angiographic Vitamin and Estrogen Trial (WAVE), a multicenter trial of hormone replacement and antioxidant vitamin therapy in postmenopausal women with established coronary artery disease. Average particle sizes for high-density lipoprotein (HDL), low-density lipoprotein (LDL), and very low-density lipoprotein were measured by nuclear magnetic resonance in these women, and angiography was performed at baseline and followup. RESULTS: Adjusted for age, race, diabetes, smoking, blood pressure, and use of lipid-lowering and antihypertensive medications, non-White women had larger LDL particle size (difference .2 nm, 95% CI .1-.3 nm) and HDL particle size (difference.2 nm, 95% CI .1-.2 nm). Neither angiographic disease progression nor survival without myocardial infarction (median follow-up time of 2.8 years) was associated with lipoprotein particle size or race. CONCLUSIONS: Non-White women have a less atherogenic profile of lipoprotein particle sizes than do White women. However, this difference did not affect event-free survival or angiographic progression of coronary atherosclerosis.
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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 | 0.001 |
| 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".