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Record W4392606939 · doi:10.1016/j.gimo.2024.100897

P020: Recommendations from the ClinGen Peroxisomal Variant Curation Expert Panel for variant classification in ABCD1

2024· article· en· W4392606939 on OpenAlexaff
Shruthi Mohan, Irene De Biase, Tatiana Yuzyuk, Alexa Dickson, Tiziano Pramparo, Stephanie Francis, Meredith Weaver, Raquel Fernández, Sharon F. Suchy, Ann B. Moser, Rong Mao, Nancy Braverman

Bibliographic record

VenueGenetics in Medicine Open · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsPeroxisomeComputational biologyPeroxisomal disorderGeneticsComputer scienceNatural language processingBiologyGene

Abstract

fetched live from OpenAlex

Peroxisomal disorders encompass a clinically and genetically heterogeneous group of disorders caused by impaired peroxisome biogenesis or function. The most common, Adrenoleukodystrophy (ALD), caused by pathogenic variants in ABCD1 gene (OMIM 300371) on the X chromosome, has been included in newborn screening programs in 39 US states since 2013. This has dramatically increased the presymptomatic detection of ALD and other peroxisomal disorders. Furthermore, genetic confirmation with next-generation sequencing has led to a rising number of variants in genes causing peroxisomal disorders, which need to be properly classified.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.103
GPT teacher head0.380
Teacher spread0.277 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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