Bibliographic record
Abstract
PURPOSE OF REVIEW: The 2019 European Society of Cardiology/European Atherosclerosis Society Guidelines concluded that apolipoprotein B (apoB) was a more accurate measure of cardiovascular risk and a better guide to the adequacy of lipid lowering than low-density lipoprotein cholesterol (LDL-C) or non-high-density lipoprotein cholesterol (HDL-C). Also, they stated that apoB can be measured more accurately than LDL-C or non-HDL-C. This strong endorsement of the central role of apoB contrasts with the limited endorsement of apoB by the 2018 American College of Cardiology/American Heart Association Multisociety Guidelines. Nevertheless, both retained LDL-C as the primary metric to guide statin/ezetimibe/Proprotein convertase subtilisin/kexin type 9 (PCSK9) therapy. RECENT FINDINGS: This essay will review the most important recent advances in knowledge about apoB with particular emphasis on the results of Mendelian randomization studies and a new discordance analysis in subjects on statin therapy. We will also lay out why using LDL-C to guide the adequacy of lipid lowering therapy represents an interpretive error of the results of the statin/ezetimibe/PCSK9 inhibitor randomized clinical trials and therefore why apoB should be the primary metric to guide statin/ezetimibe/PCSK9 therapy. SUMMARY: There is now a robust body of evidence demonstrating the superiority of apoB over LDL-C and non-HDL-C as a clinical marker of cardiovascular risk. LDL-C is not the appropriate marker to assess the benefits of statin/ezetimibe/PCSK9 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".