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Record W2562749747 · doi:10.21767/2171-6625.100045

Diagnostic Value of Neurophysiological Evaluation in Patients with ARSACS

2015· article· en· W2562749747 on OpenAlexaboutno aff
Jun Tsugawa, Shinji Ouma, Jiro Fukae

Bibliographic record

VenueJournal of Neurology and Neuroscience · 2015
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePeripheral neuropathyPathologyElectrophysiologyNeuroscienceInternal medicineEndocrinologyPsychology

Abstract

fetched live from OpenAlex

Title: Diagnostic value of neurophysiological evaluation in patients with ARSACS. Background: Autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) is characterized by early-onset spastic ataxia with congenital deformity of the extremities and retinal striation. Although it has been known that ARSACS is frequently associated with peripheral neuropathy, it has not been described in detail using electrophysiology. Methods and finding: We report the clinical, electrophysiological, and nerve ultrasonographic findings of three patients with ARSACS. Our patients exhibited electrophysiological signs of both myelinopathy and axonopathy, predominantly affecting the lower limbs, with slow MCV and prolonged F wave latency. These results suggested that the characteristics of neuropathy in ARSACS might arise from primary length-dependent peripheral myelinopathy associated with secondary axonal injury that worsened over a long period. Nerve ultrasonography revealed slight nerve enlargement, also suggestive of peripheral nerve demyelnation. These findings indicated that the peripheral neuropathy observed in patients with ARSACS might predominantly be demyelinating in origin with patchy demyelination. Conclusion: These electrophysiological and ultrasonographical observations might be helpful for the accurate diagnosis of ARSACS because clinical features of ARSACS are diverse, including atypical cases with a spasticity-lacking phenotype or cases without a family history.

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)0.000
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.472
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.047
GPT teacher head0.283
Teacher spread0.236 · 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 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

Citations0
Published2015
Admission routes1
Has abstractyes

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