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Record W1886620412 · doi:10.1002/mus.24926

Reliability of measures of quadriceps muscle function using magnetic stimulation

2015· article· en· W1886620412 on OpenAlexaff
Paul J. Tofari, David A. Opar, Justin G. Kemp, François Billaut, Stuart J. Cormack

Bibliographic record

VenueMuscle & Nerve · 2015
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsIntraclass correlationElectromyographyReliability (semiconductor)Standard errorMedicineStimulationPhysical medicine and rehabilitationCoefficient of variationConfidence intervalPhysical therapyReproducibilityInternal medicineMathematicsStatisticsPhysics

Abstract

fetched live from OpenAlex

INTRODUCTION: Magnetic stimulation can be used to assess muscle function by calculating voluntary activation using an interpolated twitch during maximal voluntary contractions (MVCs) and control twitches to potentiated muscle. In this study we assessed the reliability of torque, electromyography (EMG), and voluntary activation variables. METHODS: Fifteen men completed 5 testing sessions (2 familiarization and 3 reliability trials) to assess quadriceps femoris muscle function. Intra- and interday reliability levels of torque and EMG variables were estimated using typical error ± 90% confidence limits, expressed as percentage [coefficient of variation (CV)] and intraclass correlation coefficient. The smallest worthwhile change was calculated as 0.2 × between-participant standard deviation. RESULTS: Intra- and interday torque variables for MVC were reliable (CV < 4%, ICC 0.98, and CV < 5%, ICC 0.99, respectively). EMG variables were less reliable than torque variables, with CVs ranging from 7% to 18%. CONCLUSION: Magnetic stimulation of the femoral nerve is a reliable method for assessing muscle function.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.931
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

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

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 head0.044
GPT teacher head0.241
Teacher spread0.197 · 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

Citations12
Published2015
Admission routes1
Has abstractyes

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