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Record W2921707010 · doi:10.1016/j.cjco.2019.02.005

Efficacy of Evolocumab in Monogenic vs Polygenic Hypercholesterolemia

2019· article· en· W2921707010 on OpenAlexafffundabout
Timothy Lee, Michael A. Iacocca, Matthew R. Ban, Robert A. Hegele

Bibliographic record

VenueCJC Open · 2019
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsWestern University
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of Canada
KeywordsEvolocumabFamilial hypercholesterolemiaKexinMedicinePCSK9Proprotein convertaseInternal medicineEndocrinologyGastroenterologyLipoproteinCholesterolLDL receptor

Abstract

fetched live from OpenAlex

BACKGROUND: Inhibitors of proprotein convertase subtilisin kexin 9 are indicated in Canada for treatment of patients with familial hypercholesterolemia (FH). Classically, FH is considered to be a monogenic condition caused by rare pathogenic mutations; however, some patients have hypercholesterolemia on a polygenic basis. Whether the effect of proprotein convertase subtilisin kexin 9 inhibitor treatment differs between patients with monogenic hypercholesterolemia and patients with polygenic hypercholesterolemia is unclear. METHODS: We performed retrospective chart reviews on patients treated with evolocumab 140 mg subcutaneously biweekly from the Lipid Genetics Clinic, London Health Sciences Centre. Evolocumab-treated patients with hypercholesterolemia were grouped into monogenic or polygenic categories on the basis of their genotype determined by targeted next-generation sequencing. Absolute and relative changes in low-density lipoprotein cholesterol (LDL-C) levels before and after evolocumab treatment were studied. RESULTS: = not significant). CONCLUSION: Although the sample size is small, the findings suggest comparable biochemical responsiveness to evolocumab in both monogenic (heterozygous) and polygenic hypercholesterolemia.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.358
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.017
GPT teacher head0.298
Teacher spread0.282 · 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 designObservational
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

Citations8
Published2019
Admission routes3
Has abstractyes

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