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Record W2744401277 · doi:10.1002/mus.26500

Balance impairment in pediatric charcot–marie–tooth disease

2019· article· en· W2744401277 on OpenAlexfundno aff
Tim Estilow, AM Glanzman, Joshua Burns, Emanuela Pagliano, Davide Pareyson, Mary M. Reilly, RS Finkel

Bibliographic record

VenueMuscle & Nerve · 2019
Typearticle
Languageen
FieldNeuroscience
TopicHereditary Neurological Disorders
Canadian institutionsnot available
FundersNational Institute of Neurological Disorders and StrokeMedical Research CouncilUniversity of SydneyUniversity College LondonNational Institute for Health and Care ResearchNational Health and Medical Research CouncilNatural Sciences and Engineering Research Council of CanadaMuscular Dystrophy AssociationCharcot-Marie-Tooth Association
KeywordsTooth diseaseBalance (ability)MedicinePhysical medicine and rehabilitationDiseaseInternal medicine

Abstract

fetched live from OpenAlex

ABSTRACT Introduction : Balance impairment contributes to gait dysfunction, falls, and reduced quality of life in adults with Charcot–Marie–Tooth disease (CMT) but has been minimally examined in pediatric CMT. Methods : The CMT Pediatric Scale (CMTPedS) was administered to 520 children with CMT. Associations between balance function (Bruininks–Oseretsky Test of Motor Proficiency [BOT‐2]) and sensorimotor and gait impairments were investigated. Results : Daily trips/falls were reported by 42.3% of participants. Balance (BOT‐2) varied by CMT subtype, was impaired in 42% of 4‐year‐olds, and declined with age ( P < 0.001). Vibration ( P < 0.001), pinprick ( P < 0.004), ankle dorsiflexion strength ( P < 0.001), and foot alignment ( P < 0.004) were associated with BOT‐2 balance (adjusted R 2 = 0.28). The visual dependence of balance increased with age. Discussion : Balance impairment occurs from a young age in children with CMT. Balance intervention studies are required in pediatric CMT and should consider the degree of sensorimotor impairment, foot malalignment, and visual dependence. Muscle Nerve , 2019

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.001

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 head0.017
GPT teacher head0.234
Teacher spread0.217 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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