Evidence for changing lipid management strategy to focus on non-high density lipoprotein cholesterol
Bibliographic record
Abstract
Low-density lipoprotein cholesterol (LDL-C) has been recommended as the primary treatment target on lipid management in coronary heart disease (CHD) patients for past several decades. However, even by aggressive LDL-C lowering treatment, patients still present a significant residual risk of major adverse cardiovascular events (MACE). Non-high-density lipoprotein cholesterol (non-HDL-C) contained all the atherogenic lipoproteins, such as chylomicron, very-low density lipoprotein (VLDL), LDL, intermediate density lipoprotein (IDL). Many prospective observation studies have found that non-HDL-C was better than LDL-C in predicting risks of MACE. Since non-HDL-C appears to be superior for risk prediction beyond LDL-C, current guidelines have emphasize the importance of non-HDL-C for guiding cardiovascular prevention strategies and have flagged non-HDL-C as a co-primary therapeutic target. The goals of non-HDL-C were recommended as 30 mg/dl higher than the corresponding LDL-C goals, but the value seemed inappropriate. This review provide evidence for changing lipid management strategy to focus on non-HDL-C and appropriate values for adding to LDL-C goals would be proposed.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".