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Record W3156740020 · doi:10.1097/mol.0000000000000754

Update on apolipoprotein B

2021· article· en· W3156740020 on OpenAlexaff

Bibliographic record

VenueCurrent Opinion in Lipidology · 2021
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsApolipoprotein BApolipoprotein ETriglycerides bloodApolipoprotein A1

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.820
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.065
GPT teacher head0.364
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations51
Published2021
Admission routes1
Has abstractyes

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