<scp>Ultrasound‐guided</scp> motor unit scanning electromyography
Bibliographic record
Abstract
INTRODUCTION/AIMS: Measuring the spatial dimensions of a single motor unit remains a challenging problem, and current techniques, such as scanning electromyography (EMG), tend to underestimate the true dimensions. In this study we aimed to estimate more accurately the dimensions of a single motor unit by developing a clinically applicable scanning EMG protocol that utilizes ultrasound imaging to visualize and target a transect through the center of a single motor unit. METHODS: Single motor unit twitches in the tibialis anterior muscles of healthy volunteers were elicited via stimulation of the fibular nerve, visualized with ultrasound, and targeted with an intramuscular EMG electrode. The electrode was moved by hand in small steps through the motor unit territory. Ultrasound video output was synchronized to EMG capture, and the needle position was tracked at each step. RESULTS: Eight recordings from six participants were collected. The technique was quick and easy to perform (mean time, 6.1 minutes) with reasonable spatial resolution (mean step size, 1.85 mm), yielding motor unit territory sizes between 1.53 and 14.65 mm (mean, 7.15 mm). DISCUSSION: Ultrasound-guided motor unit scanning EMG is a quick and accurate method for obtaining a targeted motor unit transect. This combination of two readily available clinical tools provides insights into the dimensions and internal structure of the motor unit as a marker for neuromuscular conditions.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".