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Record W2892626660 · doi:10.1002/mus.26352

Magnetic resonance imaging of the anterior compartment of the lower leg is a biomarker for weakness, disability, and impaired gait in childhood Charcot–Marie–Tooth disease

2018· article· en· W2892626660 on OpenAlexfundno aff
Kayla Cornett, Elizabeth Wojciechowski, Amy D. Sman, Terri Walker, Manoj P. Menezes, Paula Bray, Mark Halaki, Joshua Burns

Bibliographic record

VenueMuscle & Nerve · 2018
Typearticle
Languageen
FieldNeuroscience
TopicHereditary Neurological Disorders
Canadian institutionsnot available
FundersMedical Research Council CanadaMuscular Dystrophy Association
KeywordsMagnetic resonance imagingGaitMedicineWeaknessPhysical medicine and rehabilitationMuscle weaknessIntramuscular fatGait analysisAtaxic GaitPhysical therapyInternal medicineAnatomyRadiologyAtaxia

Abstract

fetched live from OpenAlex

INTRODUCTION: Biomarkers of disease severity in Charcot-Marie-Tooth disease (CMT) are required to evaluate early responses to treatment. In this study we used magnetic resonance imaging (MRI) to evaluate the relationship between muscle volume and intramuscular fat accumulation with weakness, disability, and impaired gait in affected children and adolescents. METHODS: Fifty-five participants underwent MRI of the anterior compartment of the lower leg. Muscle and fat volumes were calculated. Strength was measured using hand-held dynamometry, disability using the CMT Pediatric Scale, and 3-dimensional gait analysis using an 8-camera Vicon Nexus motion capture system. RESULTS: Lower muscle volume was significantly associated with reduced dorsiflexion strength, increased disability, impaired gait profile score, and foot drop. Intramuscular fat accumulation was associated with reduced dorsiflexion strength and impaired gait profile score. DISCUSSION: The MRI protocol described was feasible, reliable, and sensitive to the magnitude of weakness, disability, and walking difficulties in children with CMT. Muscle Nerve 59:213-217, 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.022
GPT teacher head0.252
Teacher spread0.231 · 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.

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

Citations17
Published2018
Admission routes1
Has abstractyes

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