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Record W4413307840 · doi:10.1186/s13256-025-05480-z

Identification of a novel SACS gene mutation leading to spastic ataxia Charlevoix-Saguenay type: a case report

2025· article· en· W4413307840 on OpenAlexaboutno aff
Víctor Raggio, Andrea Rey, Camila Simoes, Florencia Birriel, Soledad Rodríguez, Kateryn Bentancor, Alejandra Tapié, Lucía Spangenberg

Bibliographic record

VenueJournal of Medical Case Reports · 2025
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsnot available
FundersH2020 European Research CouncilAgencia Nacional de Investigación e Innovación
KeywordsMedicineIdentification (biology)GeneticsGeneAtaxiaMutationBioinformaticsSurgical oncologyComputational biologyBiologyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Spastic ataxia Charlevoix-Saguenay is a rare autosomal recessive neurodegenerative disorder characterized by a combination of spasticity, ataxia, and peripheral neuropathy. Although predominantly affecting individuals of French-Canadian descent, the geographic distribution of spastic ataxia Charlevoix-Saguenay-associated cases is expanding. CASE PRESENTATION: This study presents the case of a 3-year-old Uruguayan girl with suspected autosomal recessive spastic ataxia of Charlevoix-Saguenay, demonstrating the disease's presence in previously unreported locations. Exome sequencing analysis revealed two compound heterozygous variants in the sacsin molecular chaperone gene, one of which was novel. CONCLUSION: This report highlights the genomic heterogeneity of spastic ataxia Charlevoix-Saguenay and emphasizes the importance of investigating the genetic landscape of the disease in diverse populations. Understanding the underlying genetic alterations and their geographic distribution contributes to improved diagnosis, management, and potentially targeted therapies for individuals affected by spastic ataxia Charlevoix-Saguenay worldwide.

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)0.002
metaresearch head score (Gemma)0.030
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.034
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.042
GPT teacher head0.349
Teacher spread0.307 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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