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Record W4407829517 · doi:10.1212/nxg.0000000000200253

Involvement of the Superior Cerebellar Peduncles in GAA- <i>FGF14</i> Ataxia

2025· article· en· W4407829517 on OpenAlexaff
Shihan Chen, Catherine Ashton, Rawan Sakalla, Guillemette Clément, Sophie Planel, Céline Bonnet, Phillipa J. Lamont, Karthik Kulanthaivelu, Atchayaram Nalini, Henry Houlden, Antoine Duquette, Marie-Josée Dicaire, Pablo Iruzubieta, Javier Ruiz‐Martínez, Erin P. Lucas, Rodrigo Sutil Berjon, Jon Infante, Elisabetta Indelicato, Sylvia Boesch, Matthis Synofzik, Benjamin Bender, Matt C. Danzi, Stephan Züchner, David Pellerin, Bernard Brais, Mathilde Renaud, Roberta La Piana

Bibliographic record

VenueNeurology Genetics · 2025
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsCentre Hospitalier de l’Université de MontréalMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsAtaxiaNeuroscienceCerebellar ataxiaPsychologyPhysical medicine and rehabilitationMedicine

Abstract

fetched live from OpenAlex

Objectives: gene. After the clinical observation of superior cerebellar peduncle (SCP) involvement in some affected patients, we sought to verify the prevalence of this finding in our cohort and 4 additional independent cohorts of patients with SCA27B. Methods: We performed a retrospective review of the brain MRI scans of a total of 87 patients (median age at MRI 69 years; range 28-88 years) from different independent cohorts to assess the presence of SCP involvement, defined as abnormally high T2 signal along the SCP tract. Results: We observed SCP involvement in 52 patients (52/87; 59.8%) from all the cohorts combined. The finding was replicated at rates ranging from 50% to 62.8% in the cohorts taken separately. Discussion: SCP involvement in SCA27B is frequent. Its detection may facilitate the diagnostic process of patients with SCA27B.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.261
Teacher spread0.238 · 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 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

Citations16
Published2025
Admission routes1
Has abstractyes

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