Low-density lipoprotein-lowering strategies
Bibliographic record
Abstract
PURPOSE OF REVIEW: Maximalist low-density lipoprotein (LDL)-lowering strategies such as lowering LDL as much as possible or, alternatively, using the most potent LDL-lowering regimens have become increasingly popular. Almost all attention has focused on the potential advantages of these approaches with little focus on their potential disadvantages. Moreover, it is increasingly assumed that the lower and lower is better and better approach is supported by unassailable evidence. RECENT FINDINGS: This article will examine how strongly the findings of the statin clinical trials actually support the maximalist strategy. We will also introduce a new approach, the population percentile strategy, which is based on the fact that the amount of cholesterol in LDL can differ substantially. When cholesterol-depleted LDL particles are present, LDL cholesterol (LDL-C) underestimates apolipoprotein B (apoB) and LDL particle number. Statins lower LDL-C and nonhigh-density lipoprotein cholesterol (non-HDL-C) more than they lower apoB and LDL particle number. This means that, even if LDL-C, non-HDL-C and apoB are equal markers of on-treatment risk, apoB is a better marker of the adequacy of LDL-lowering therapy. SUMMARY: Our analysis indicates that the LDL-lowering regimen should be tailored to the individual using a population percentile strategy to ensure the greatest number of patients receive the greatest overall benefit. With this approach, apoB is the best marker of the adequacy of LDL-lowering therapy.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".