Impact of reference electrode position on motor unit number estimation (MUNE) in the tibialis anterior muscle using MScanFit: test-retest reliability
Bibliographic record
Abstract
Abstract Objectives This study aimed to assess the effect of varying the reference electrode position, specifically comparing position A3 (medial patella) to routine position 1 (R1) and the MScan multicenter protocol position (M1), on compound muscle action potential (CMAP) and motor unit number estimation (MUNE) in the tibialis anterior muscle of healthy participants. Methods Twenty healthy participants underwent repeated MScanFit MUNE assessments with a 7-14 day interval between tests. The reference electrode (E2) was placed in three positions at each visit (A3, R1, and M1), while the active electrode (E1) remained constant. An additional seventeen participants were included to establish the minimal detectable true change in MUNE values using MScanFit, with the reference electrode exclusively in the M1 position. Results The reference electrode position significantly influenced CMAP and MUNE, with R1 resulting in lower values. However, no significant difference was observed between M1 and A3 positions. Relative and absolute reliability indicators favored using the M1 position for reference in MScanFit MUNE. In a combined dataset of 37 healthy participants, the average tibialis anterior muscle motor unit count was estimated at 148 (SD 25.2), with a minimal detectable true change of 55 units. Conclusions The preference for the M1 position over the alternative A3 position is supported, particularly for MScanFit MUNE assessments in the tibialis anterior muscle. Clinically, a true change in MUNE should consider the minimal detectable change of 55 motor units, underscoring the reality that large changes in MUNE are required to conclude a genuine change beyond measurement error. Significance For MUNE examinations of the tibialis anterior muscle, adhering to the electrode positions outlined in the MScan multicenter protocol is advisable. Awareness of measurement error limitations in MScanFit MUNE underscores its applicability in making longitudinal clinical decisions for individual patients.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".