Evaluating motor unit properties after nerve transfer surgery
Bibliographic record
Abstract
Nerve transfer surgery (NTS) shows promise in restoring movement to muscles paralyzed by spinal cord (SCI) and peripheral nerve injury (PNI). Yet, motor outcomes vary, and the neurophysiological factors influencing responders and non-responders remain unclear. As the fundamental goal of NTS is to reinnervate paralyzed muscles by creating new motor units (MUs), we examined MU properties after NTS for individuals with SCI and PNI. Nine participants (3 SCI, 6 PNI, 50.3 ± 13.9 years) >18 months post-NTS were evaluated and compared to either age-matched controls (SCI) or the contralateral limb (PNI). We used a sophisticated, signal processing software known as Decomposition-Based Quantitative Electromyography (DQEMG) and near-fiber EMG to examine MU characteristics sampled from needle electromyography signals recorded during low-intensity contractions. The NTS muscle MU potentials (MUP) were larger, and near-fiber MUPs (NFM) were more temporal dispersed than controls. Measures of neuromuscular junction instability were greater in NTS muscles compared to controls (p < 0.05). Firing rates of MU, and MUP phases and turns were not different between groups (p > 0.05). Overall, these data suggest the quality of reinnervation post-surgery requires further investigation as a potential mediator of motor outcome and the required time for successful reinnervation may be longer than currently predicted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".