Comparing motor unit number estimation techniques
Bibliographic record
Abstract
It is unclear how comparable motor unit number estimates (MUNEs) are when derived from a non-invasive technique involving repetitive peripheral nerve stimulation vs. one involving volitional contractions and intramuscular recordings of single motor units (MUs). Therefore, this study examined MUNEs from MScanFit (MScan) and Decomposition-Enhanced Spike-Triggered Averaging (DE-STA). Eighteen participants (8 females, 10 males; 29.7 ± 7.1 years) sat with their right leg positioned in an isometric myograph while surface electromyography (EMG) was recorded from the tibialis anterior (TA). The MScan protocol isolated and derived the size of single MUs by repeatedly stimulating the common fibular nerve at progressively weaker currents to model a compound muscle action potential (CMAP) stimulus-response curve. For DE-STA, a concentric needle electrode was inserted into the TA, and participants performed 30-s isometric dorsiflexion contractions at 25 % of maximal voluntary torque to obtain ≥20 individual surface MU potentials (S-MUPs; i.e., single MUs extracted from the surface EMG signal based on needle-detected spikes). Both techniques used the same maximal CMAP to calculate a MUNE, yet MScan used a mathematical model to simulate the recorded CMAP stimulus-response, which was compared to the recorded scan to minimize disagreement; whereas DE-STA compared the size of the maximal CMAP to the average S-MUP. There was no difference between the MUNE calculated via DE-STA (132 ± 26 MUs) and MScan (142 ± 22 MUs; p = 0.11), and the bias (10.0 MUs) and limits of agreement (67.6 vs -47.6 MU difference) suggests that either technique may independently offer a reasonable MU estimate for the TA of young adults.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".