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Record W3186499020 · doi:10.1177/2329048x211027438

A Novel <i>De Novo</i> Variant in <i>DYNC1H1</i> Causes Spinal Muscular Atrophy Lower Extremity Predominant in Identical Twins: A Case Report

2021· article· en· W3186499020 on OpenAlexafffund
Alexa Derksen, Amytice Mirchi, Luan T. Tran, Lei Cao-Lei, Maryam Oskoui, Myriam Srour, Chantal Poulin, Geneviève Bernard

Bibliographic record

VenueChild Neurology Open · 2021
Typearticle
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersFonds de Recherche du Québec - Santé
KeywordsSpinal muscular atrophyMotor neuronSpinal cordExome sequencingMedicinePhenotypeLower motor neuronWeaknessAtrophyUpper motor neuronAnatomyNeurosciencePathologyBiologyGeneticsAmyotrophic lateral sclerosisGene

Abstract

fetched live from OpenAlex

Mutations in DYNC1H1 have been shown to cause spinal muscular atrophy lower extremity predominant type 1 (SMALED1), an autosomal dominant genetic neuromuscular disorder characterized by degeneration of spinal cord motor neurons resulting in muscle weakness. Here, we describe monozygotic twins, one with a more severe upper motor neuron phenotype as a result of a suspected perinatal hypoxic-ischemic event and the other presenting a typical lower motor neuron phenotype. Using exome sequencing, we identified the novel de novo variant c.752G>T; p.Arg251Leu in DYNC1H1. We thereby add this variant to the growing list of mutations in DYNC1H1 that cause SMALED1.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.027
GPT teacher head0.327
Teacher spread0.300 · 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 designCase report
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

Citations2
Published2021
Admission routes2
Has abstractyes

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