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Record W4324117346 · doi:10.1016/j.gimo.2023.100499

P452: Specifying the ACMG/AMP variant sequence interpretation guidelines for congenital myopathies*

2023· article· en· W4324117346 on OpenAlexaff
Marina T. DiStefano, Ryan Webb, Hannah McCurry, Shannon McNulty Gray, Swati Tomar, Prasad Kopparapu, Eleanor C Broeren, Kezang Tshering, Alan H. Beggs, Enrico Bertini, Adele D’Amico, Sandra Donkervoort, James J. Dowling, Fabiana Fattori, Ana Ferreiro, Casie A. Genetti, Hernán Gonorazky, Svetlana Gorokhova, Amanda Lindy, Līvija Medne, Sander Pajusalu, Katarina Pelin, John Rendu, Matteo Vatta, Tom Winder, Hui Yang, Grace Yoon, Ozge Ceyhan‐Birsoy, Carsten G. Bönnemann

Bibliographic record

VenueGenetics in Medicine Open · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsInterpretation (philosophy)Sequence (biology)Computational biologyComputer scienceMedicineBioinformaticsGeneticsBiologyProgramming language

Abstract

fetched live from OpenAlex

Congenital myopathies are a group of neuromuscular disorders that typically present at birth with hypotonia and muscle weakness, with an estimated prevalence of ∼1-5/100,000 live births. To date, over 30 genes have been associated with various forms of congenital myopathy and 29 of them have been defined as having “Definitive” evidence for causality by The Clinical Genome Resource (ClinGen), Congenital Myopathies Gene Curation Expert Panel (CM GCEP). Many of these genes have up to several hundred variants reported in patients with varying amounts of data in the literature supporting their association with disease, which poses a challenge in the clinical setting.

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 imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.055
Threshold uncertainty score0.185

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.045
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0030.002
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0550.047

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 head0.194
GPT teacher head0.440
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations0
Published2023
Admission routes1
Has abstractyes

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