Normative Extensor Hallucis Brevis Muscle Activity During Locomotion Following the Development of a Novel Ultrasound-Guided Fine-Wire Electromyography Protocol
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".