LDL particle number and size and cardiovascular risk: anything new under the sun?
Bibliographic record
Abstract
PURPOSE OF REVIEW: We provide here an up-to-date perspective on the potential use of LDL particle number and size as complementary risk factors to predict and manage cardiovascular disease (CVD) risk in the clinical realm. RECENT FINDINGS: Studies show that a significant proportion of the population has discordant LDL particle number and cholesterol indices [non-HDL cholesterol (HDL-C)]. Data also show that risk prediction may be improved when using information on LDL particle number in patients with discordant particle number and cholesterol data. Yet, most of the current CVD guidelines conclude that LDL particle number is not superior to cholesterol indices, including non-HDL-C concentrations, in predicting CVD risk. LDL particle size, on the other hand, has not been independently associated with CVD risk after adjustment for other risk factors such as LDL cholesterol, triglycerides, and HDL-C and that routine use of information pertaining to particle size to determine and manage patients' risk is not yet justified. SUMMARY: Additional studies are required to settle the debate on which of cholesterol indices and LDL particle number is the best predictor of CVD risk, and if such measures should be integrated in clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".