High density lipoproteins: Measurement techniques and potential biomarkers of cardiovascular risk
Bibliographic record
Abstract
Plasma high density lipoprotein cholesterol (HDL) comprises a heterogeneous family of lipoprotein species, differing in surface charge, size and lipid and protein compositions. While HDL cholesterol (C) mass is a strong, graded and coherent biomarker of cardiovascular risk, genetic and clinical trial data suggest that the simple measurement of HDL-C may not be causal in preventing atherosclerosis nor reflect HDL functionality. Indeed, the measurement of HDL-C may be a biomarker of cardiovascular health. To assess the issue of HDL function as a potential therapeutic target, robust and simple analytical methods are required. The complex pleiotropic effects of HDL make the development of a single measurement challenging. Development of laboratory assays that accurately HDL function must be developed validated and brought to high-throughput for clinical purposes. This review discusses the limitations of current laboratory technologies for methods that separate and quantify HDL and potential application to predict CVD, with an emphasis on emergent approaches as potential biomarkers in clinical practice.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".