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Record W4413986030 · doi:10.1080/21678421.2025.2555218

A <i>VAPB</i> (P56S) mutation in a Dutch patient with familial motor neuron disease: a case report

2025· article· en· W4413986030 on OpenAlexaff
Sean W. Willemse, Koen C. Demaegd, Ruben P. A. van Eijk, Philip Van Damme, Elizabeth A. Harrington, Matthew B. Harms, Neil A. Shneider, Wouter van Rheenen, Jan H. Veldink, Leonard H. van den Berg, Michael A. van Es

Bibliographic record

VenueAmyotrophic Lateral Sclerosis and Frontotemporal Degeneration · 2025
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsColumbia College
FundersZonMw
KeywordsMotor neuronDiseaseMutationMedicineGeneticsNeurosciencePsychologyBiologyInternal medicineGene

Abstract

fetched live from OpenAlex

The c.166C > T p.(Pro56Ser) or P56S mutation in the VAPB gene was initially identified as a cause of motor neuron disease in Brazil in a large extended pedigree comprising >1,500 individuals including more than 200 cases. This VAPB mutation gives rise to three phenotypes: late-onset spinal muscular atrophy, classical ALS with bulbar involvement, pyramidal signs and rapid disease progression, and atypical ALS with slow progression. Nearly all known cases originate from a single founder, with most cases outside of Brazil being related to this pedigree. However, there is one report of an independent German family with the same mutation on a different haplotype, indicating a second founder event. Here, we report the first Dutch patient with a P56S mutation in VAPB and motor neuron disease. Documenting rare genetic causes of MND and their natural history are of increasing importance in light of emerging gene-specific therapies.

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.001
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 head0.018
GPT teacher head0.253
Teacher spread0.235 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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