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Record W2988883666 · doi:10.1002/mus.26758

Quantitative sonographic evaluation of muscle thickness and fasciculation prevalence in healthy subjects

2019· article· en· W2988883666 on OpenAlexaff
Alon Abraham, Vivian E. Drory, Yaara Fainmesser, Leif Erik Lovblom, Vera Bril

Bibliographic record

VenueMuscle & Nerve · 2019
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity Health NetworkSinai Health SystemLunenfeld-Tanenbaum Research InstituteUniversity of Toronto
Fundersnot available
KeywordsFasciculationMedicineBicepsPercentileAnatomyUltrasoundRadiology

Abstract

fetched live from OpenAlex

BACKGROUND: In the current study, we aimed to determine normative values for muscle thickness and fasciculation prevalence in healthy subjects. METHODS: We performed a prospective study from October to December 2018 in 65 healthy subjects. All subjects underwent quantitative sonographic evaluation of muscle thickness and fasciculation prevalence in the following 8 muscles: Biceps brachii, abductor pollicis brevis, first dorsal interosseous, abductor digiti minimi, quadriceps, tibialis anterior, extensor digitorum brevis, and abductor hallucis brevis. RESULTS: Subject ages ranged from 21 to 82 years, with 63% women. Normative values for muscle thickness were determined using the fifth percentile. Multivariate regression analysis showed that sex, age, body mass index, and hand dominance affected muscle thickness. Fasciculations were observed frequently only in distal muscles. CONCLUSIONS: Normal values for muscle thickness were determined, and may enhance neuromuscular ultrasound sensitivity and serve as a basis for future studies. Larger series are needed to confirm these values.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.548
Threshold uncertainty score0.537

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.028
GPT teacher head0.274
Teacher spread0.246 · 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

Citations23
Published2019
Admission routes1
Has abstractyes

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