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Record W4224941018 · doi:10.2217/fca-2021-0149

Lessons Learned From the Evinacumab Trials in the Treatment of Homozygous Familial Hypercholesterolemia

2022· review· en· W4224941018 on OpenAlexaff
Étienne Khoury, Laurent Croteau, Alex Lauzière, Daniel Gaudet

Bibliographic record

VenueFuture Cardiology · 2022
Typereview
Languageen
FieldMedicine
TopicLipid metabolism and disorders
Canadian institutionsCégep de ChicoutimiUniversité de MontréalUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsMedicineFamilial hypercholesterolemiaInternal medicineClinical trialPediatricsCholesterol

Abstract

fetched live from OpenAlex

Homozygous familial hypercholesterolemia (HoFH) is a life-threatening disease characterized by extremely elevated LDL cholesterol (LDL-C) levels which result in premature atherosclerotic cardiovascular disease. As conventional lipid-lowering therapies, which mainly depend on LDL receptors for LDL particle clearance, remain insufficient for reaching the recommended LDL-C levels in HoFH, agents acting independently of LDL receptors, such as ANGPTL3 inhibitors, constitute a promising target. Evinacumab, a monoclonal antibody directed against ANGPTL3, was approved in the USA in 2021 for treating patients with HoFH. Evinacumab has shown an adequate safety profile with strong LDL-lowering efficacy. This review highlights the development path of evinacumab and provides insight on the lessons learned from trials as well as the hurdles facing accessibility.

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)0.002
metaresearch head score (Gemma)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score0.860

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.249
GPT teacher head0.420
Teacher spread0.171 · 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
GenreReview

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

Citations7
Published2022
Admission routes1
Has abstractyes

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