Fifty-three year follow-up of coronary heart disease versus HDL2 and other lipoproteins in Gofman's Livermore Cohort
Bibliographic record
Abstract
To assess the relationships of lipoprotein mass concentrations to all-cause and coronary heart disease (CHD) mortality, we analyzed the prospective 53-year follow-up of 1,905 men measured for lipoprotein mass concentrations by analytic ultracentrifugation between 1954 and 1957. Cause of death was determined from medical records and death certificates before 1979 and from National Death Index death diagnoses thereafter. Of the 1,329 men (69.8%) who died through 2008, CHD was listed as a contributing cause of death for 409 men, including 113 deaths from premature CHD (age ≤ 65 years). When adjusted for age, the risk associated with the lowest HDL2 quartile increased 22% for all-cause (P = 0.001), 63% for total CHD (P < 10(-5)), and 117% for premature CHD mortality (P = 0.0001). When adjusted for standard risk factors (age, total cholesterol, blood pressure, BMI, smoking) and the lowest HDL3 quartile, the corresponding risk increases were 14% (P = 0.05), 38% (P = 0.004), and 62% (P = 0.02), respectively. Men with HDL3 ≤ 25(th) percentile had 28% greater total CHD risk (P = 0.03) and 71% greater premature CHD risk (P = 0.01). Higher LDL-mass concentrations increased total CHD risk by 3.8% (P < 10(-9)) and premature CHD risk by 6.1% (P < 10(-7)) per 10 mg/dl increase in concentration. Thus, low HDL2 is associated with increased CHD risk.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".