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Record W4319460758 · doi:10.1017/cjn.2023.20

Motor Unit Number Index of the Upper Trapezius: A Meta-Analysis and Cross-sectional Study of Its Reliability

2023· article· en· W4319460758 on OpenAlexafffundvenue
Agessandro Abrahão, Liane Phung, David Fam, Marcio Luiz Escorcio‐Bezerra, Lawrence R. Robinson, Kelvin E. Jones, Lorne Zinman

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2023
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsUniversity of AlbertaSt Joseph's Health CentreHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersUniversity of TorontoSunnybrook Foundation
KeywordsAmyotrophic lateral sclerosisIntraclass correlationMedicineTrapezius muscleReliability (semiconductor)Physical medicine and rehabilitationUpper motor neuronCross-sectional studyPhysical therapyPsychometricsDiseaseElectromyographyInternal medicinePathologyClinical psychology

Abstract

fetched live from OpenAlex

ABSTRACT: Motor unit number index of the upper trapezius (MUNIX-Trapezius) is a candidate biomarker for bulbar lower motor neuron function; however, reliability data is incomplete. To assess MUNIX-Trapezius reliability in controls, we conducted a systematic review, a cross-sectional study (n = 20), and a meta-analysis. We demonstrated a high inter- and intra-rater intraclass correlation (0.86 and 0.94, respectively), indicating that MUNIX-Trapezius is reliable with between-study variability moderated by age and MUNIX technique. With further validation, this measure can serve as a disease monitoring and response biomarker of bulbar function in the therapeutic development for amyotrophic lateral sclerosis.

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)0.013
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.014
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.134
GPT teacher head0.366
Teacher spread0.232 · 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 designMeta-analysis
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

Citations3
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques→Same topicAmyotrophic Lateral Sclerosis Research→French-language works237,207→