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Record W2063028442 · doi:10.1002/mus.24565

Electrical impedance myography in the evaluation of the tongue musculature in amyotrophic lateral sclerosis

2015· article· en· W2063028442 on OpenAlexafffund
Sanjana Shellikeri, Yana Yunusova, Jordan R. Green, Gary L. Pattee, James D. Berry, Seward B. Rutkove, Lorne Zinman

Bibliographic record

VenueMuscle & Nerve · 2015
Typearticle
Languageen
FieldMedicine
TopicBody Composition Measurement Techniques
Canadian institutionsSunnybrook Health Science CentreUniversity of Toronto
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute on Deafness and Other Communication DisordersCanadian Institutes of Health ResearchNational Institutes of HealthALS Society of Canada
KeywordsAmyotrophic lateral sclerosisElectrical impedance myographyTongueMedicineBiomarkerUpper motor neuronPathologyInternal medicineDisease

Abstract

fetched live from OpenAlex

INTRODUCTION: Electrical impedance myography (EIM) quantifies muscle health and is used as a biomarker of muscle abnormalities in neurogenic and myopathic diseases. EIM has yet to be evaluated in the tongue musculature in patients with amyotrophic lateral sclerosis (ALS), who often show clinical bulbar signs. METHODS: The lingual musculature of 19 subjects with motor neuron disease and 21 normal participants was assessed using EIM, strength and endurance testing, and clinical assessment. RESULTS: Tongue musculature in the ALS group was characterized by significantly smaller phase (Ph) and greater resistance (R) when compared with the healthy cohort. Ph and tongue endurance were correlated in the ALS group. CONCLUSIONS: EIM of tongue musculature could distinguish those with ALS from healthy controls. The demonstrated relationship between tongue function and Ph supports further testing of EIM of the tongue as a potential biomarker in ALS.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.106
GPT teacher head0.322
Teacher spread0.215 · 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

Citations38
Published2015
Admission routes2
Has abstractyes

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