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Record W4397028967 · doi:10.1155/2024/3043736

Specifications of the ACMG/AMP Variant Curation Guidelines for Hereditary Hemorrhagic Telangiectasia Genes—ENG and ACVRL1

2024· article· en· W4397028967 on OpenAlexaff
Desiree DeMille, Jamie McDonald, Carmelo Bernabéu, Hilary Racher, Carla Olivieri, Claudia Cantarini, Anna Sbalchiero, Bryony A. Thompson, Luca Jovine, Claire L. Shovlin, Sophie Dupuis‐Girod, Gaëtan Lesca, Maud Tusseau, Arupa Ganguly, Raj S. Kasthuri, Jaime Jessen, Maarten P.G. Massink, Shoji Ichikawa, Pınar Bayrak‐Toydemir

Bibliographic record

VenueHuman Mutation · 2024
Typearticle
Languageen
FieldMedicine
TopicVascular Anomalies and Treatments
Canadian institutionsImpactUniversity of Toronto
FundersNational Human Genome Research InstituteKnut och Alice Wallenbergs StiftelseBanca d'ItaliaNational Institute for Health and Care ResearchMinistero dell’Istruzione, dell’Università e della RicercaNational Cancer InstituteNIHR Imperial Biomedical Research CentreConsejo Superior de Investigaciones CientíficasVetenskapsrådet
KeywordsACVRL1TelangiectasiaStandardizationComputational biologyGeneMedical geneticsGenetic testingGeneticsBiologyBioinformaticsComputer scienceMedicinePathology

Abstract

fetched live from OpenAlex

The 2015 ACMG/AMP standards and guidelines for interpretation of sequence variants are widely used by laboratories, including for variant curation of the hereditary hemorrhagic telangiectasia (HHT) genes. However, the need for gene- and disease-specific modifications and specifications of these general guidelines to optimize and standardize variant classification was recognized at the time of publication. With this goal, the ClinGen HHT variant curation expert panel was formed. Here, we describe our recommended HHT-specific variant classification criteria and the outcomes from pilot testing of 30 variants of the ENG and ACVRL1 genes. Eight of the original ACMG/AMP rules were determined to not be applicable for ENG- or ACVRL1-related HHT or were previously recommended by ClinGen for removal, two rules were unmodified, and the remaining 18 rules were modified according to HHT specifications or previous ClinGen general recommendations. This study demonstrates the importance of HHT-specific criteria in the optimization and standardization of HHT variant classification and conflicting classification resolution.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.742
Threshold uncertainty score0.201

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 head0.143
GPT teacher head0.369
Teacher spread0.226 · 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
Published2024
Admission routes1
Has abstractyes

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