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Record W4403919467 · doi:10.1123/jab.2023-0298

Normative Extensor Hallucis Brevis Muscle Activity During Locomotion Following the Development of a Novel Ultrasound-Guided Fine-Wire Electromyography Protocol

2024· article· en· W4403919467 on OpenAlexaff
Kelly A. Robb, Keara Sutherland, Stephen D. Perry

Bibliographic record

VenueJournal of Applied Biomechanics · 2024
Typearticle
Languageen
FieldMedicine
TopicFoot and Ankle Surgery
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsElectromyographyPhysical medicine and rehabilitationMedicineUltrasoundGait

Abstract

fetched live from OpenAlex

Fine-wire electromyography (EMG) is a traditional laboratory technique to estimate muscle activity of the small foot muscles, however, recordings have not been reported from extensor hallucis brevis (EHB). As an extensor of the great toe, EHB is an important muscle when studying physiological changes associated with foot pathologies such as hallux valgus. The purpose of this study was to develop an ultrasound-guided fine-wire EMG protocol to record EHB muscle activity and report normative EMG profiles of healthy young adults during locomotion. Sixteen asymptomatic young adults completed 20 walking trials at a self-selected velocity. Ensemble averages were calculated from the time normalized linear envelopes and represented from 0% to 100% of the single stance phase of gait. EHB muscle bursts were observed between 0% and 20% of the stance phase of gait in all participants. A second burst of EMG was observed between 80% and 100% of stance in 50% of the participants. This study introduces a novel ultrasound-guided EMG protocol and normative data from EHB recordings suggest a synergistic role to anterior compartment musculature at contact. These results provide preliminary insights into understanding the functional role of EHB and may help elucidate the biomechanical factors exacerbating the progression of hallux pathologies.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.573

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 head0.025
GPT teacher head0.291
Teacher spread0.266 · 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 designBench or experimental
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

Citations1
Published2024
Admission routes1
Has abstractyes

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