Concordance and Discordance Between Non-High-Density Lipoprotein Cholesterol and Apolipoprotein B as Cardiovascular Disease Risk Markers over the Full Spectrum of Hypertriglyceridemia: A Cross-sectional Analysis of Lipid Clinic Data
Bibliographic record
Abstract
Cardiovascular disease is a leading cause of morbidity and mortality worldwide. Lipid biomarkers are frequently used for prediction of cardiovascular disease risk. Triglycerides are routinely checked in blood work, and triglycerides are a key component of lipoproteins that contribute to atherogenic plaques, which cause cardiovascular disease. High triglycerides are a common condition in the general population. The relative effect of high triglycerides on the lipid biomarkers (non-high-density lipoprotein cholesterol, and apolipoprotein B) for cardiovascular disease risk prediction is the focus of this thesis. Using cross-sectional lipid profile data from a large Lipid Clinic, we compared the correlation and concordance between non-high-density lipoprotein cholesterol and apolipoprotein B as cardiovascular disease risk markers among patients with mild, moderate, and severe hypertriglyceridemia. The findings showed that with higher triglycerides, there is lower agreement between the two biomarkers, which raises caution that they are not interchangeable, and further research is needed.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".