Traditional and Novel Markers: Target of Treatment vs Marker of Risk
Bibliographic record
Abstract
In this article we discuss lipid-related markers associated with cardiovascular (CV) risk, and emphasize the significance of low-density lipoprotein (LDL) cholesterol (LDL-C), non-high-density lipoprotein cholesterol, and apolipoprotein B 100 . LDL-C, a traditional CV risk factor, correlates directly with atherosclerotic CV disease. However, LDL-C alone, usually estimated using the Friedewald equation, might not capture the entire risk profile. Therefore, triglycerides (TGs) and lipoprotein(a) [Lp(a)] should be measured as part of a complete CV risk assessment. Although TGs represent potential markers of increased CV risk, their role as direct causal agents remains inconclusive. Elevated TG levels suggest a greater cholesterol presence in non-LDL particles, necessitating the use of non-high-density lipoprotein cholesterol or apolipoprotein B 100 , rather than solely LDL-C, to ensure an accurate CV risk assessment. Lp(a), however, is a genetically determined particle resembling LDL, linked with various significant CV diseases. Its role in CV risk is potentially because of its added inflammatory and prothrombotic properties. Certain medications (most notably proprotein convertase subtilisin/kexin type 9 inhibitors and novel small interfering RNA molecules) can reduce Lp(a) levels. Whether this confers a benefit in preventing CV outcomes requires validation from ongoing trials. Although LDL-C remains a crucial metric, health care professionals must acknowledge its limitations and understand the emerging significance of TGs and Lp(a) in CV risk assessment. This article underscores the need to reevaluate traditional lipid markers in light of emerging evidence on TGs and Lp(a) to promote a more comprehensive approach to CV risk assessment.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".