Fractional Esterification Rate of Cholesterol and Ratio of Triglycerides to HDL-Cholesterol Are Powerful Predictors of Positive Findings on Coronary Angiography
Bibliographic record
Abstract
BACKGROUND: We examined the predictive value of various clinical and biochemical markers for angiographically defined coronary artery disease (aCAD). Specifically, we assessed the value of the ratio of plasma triglyceride (TGs) to HDL-cholesterol (HDL-C) and the fractional esterification rate of cholesterol in plasma depleted of apolipoprotein B (apoB)-containing lipoproteins (FER(HDL)), a functional marker of HDL and LDL particle size. METHODS: Patients (788 men and 320 women) undergoing coronary angiography were classified into groups with positive [aCAD(+)] and negative [aCAD(-)] findings. Patient age, body mass index, waist circumference, blood pressure (BP), medications, drinking, smoking, exercise habits, and plasma total cholesterol (TC), LDL-cholesterol (LDL-C), HDL-unesterified cholesterol, HDL-C, TGs, FER(HDL), apoB, log(TG/HDL-C), and TC/HDL-C were assessed. Lipids and apoproteins were measured by standard laboratory procedures; FER(HDL) was determined by a radioassay. RESULTS: Members of the aCAD(+) group were older and had a higher incidence of smoking and diabetes than those in the aCAD(-) group. The aCAD(+) group also had higher TG, apoB, FER(HDL), and log(TG/HDL-C) and lower HDL-C values. aCAD(+) women had greater waist circumference and higher plasma TC and TC/HDL-C. aCAD(+) men, but not women, had higher plasma LDL-C. In the multivariate logistic model, the significant predictors of the presence of aCAD(+) were FER(HDL), age, smoking, and diabetes. If only laboratory tests were included in the multivariate logistic model, FER(HDL) appeared as the sole predictor of aCAD(+). Log(TG/HDL-C) was an independent predictor when FER(HDL) was omitted from multivariate analysis. CONCLUSIONS: FER(HDL) was the best laboratory predictor of the presence of coronary atherosclerotic lesions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".