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Record W3137148471 · doi:10.1101/2021.03.14.21253565

Motor Unit Number Index (MUNIX) of the Upper Trapezius: Reliability and Meta-Analysis

2021· preprint· en· W3137148471 on OpenAlexafffund
Agessandro Abrahão, Liane Phung, David Fam, Marcio Luiz Escorcio‐Bezerra, Lawrence R. Robinson, Kelvin E. Jones, Lorne Zinman

Bibliographic record

VenuemedRxiv · 2021
Typepreprint
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsUniversity of AlbertaSt Joseph's Health CentreHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersSunnybrook Foundation
KeywordsIntraclass correlationReliability (semiconductor)MedicinePhysical medicine and rehabilitationMeta-analysisPopulationStandard errorPhysical therapyConfidence intervalPsychologyStatisticsPsychometricsMathematicsClinical psychologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Motor unit number index (MUNIX) of the upper trapezius is a candidate biomarker for lower motor neuron function of the bulbar region; however, only a few studies have explored this measure in neuromuscular diseases and reliability data is incomplete. We conducted a systematic review and meta-analysis of this measure in control participants and assessed its reliability in twenty healthy volunteers. Four studies were included with heterogeneous mean-MUNIX estimates, moderated by variability in the population’s age and MUNIX sampling technique. We demonstrated an inter- and intra-rater intraclass correlation of 0.86 and 0.94, respectively. Upper trapezius MUNIX is a reliable measure with in-between study variability moderated by age and MUNIX technique.

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)0.024
metaresearch head score (Gemma)0.064
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.976
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.064
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.017
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.064
GPT teacher head0.339
Teacher spread0.275 · 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.

Study designMeta-analysis
DomainMethods
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
Published2021
Admission routes2
Has abstractyes

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