Neuromuscular recruitment pattern in motor point stimulation
Bibliographic record
Abstract
BACKGROUND: Transcutaneous electrical stimulation on the motor points over muscle belly, i.e., motor point stimulation (MPS), is widely used in clinical settings, however it is not fully understood how MPS recruits motor nerves. Here we investigated the recruitment pattern of the motor nerve and twitch force during MPS and compared to the recruitment during peripheral nerve stimulation (PNS). METHODS: Ten healthy individuals participated in this study. Using MPS on the soleus muscle and PNS on the tibial nerve, a single pulse stimulation was applied with various stimulation intensities from subthreshold to the maximum intensity. We measured the evoked potentials in the lower leg muscles and twitch force. Between MPS and PNS, we compared the recruitment curves of M-waves and the dynamics of twitch force such as duration from force onset to peak (time-to-peak). RESULTS: The maximum M-wave was not different between MPS and PNS in the soleus muscle, while it was much smaller in MPS than in PNS in the other lower leg muscles. This reflected the smaller twitch force of plantarflexion in MPS than PNS. In addition, the slope of the recruitment curve for the soleus M-wave was smaller in MPS than PNS. CONCLUSION: Therefore, unlike PNS, MPS can efficiently and selectively recruit motor nerves of the target muscle and gradually increase the recruitment of the motor nerve.
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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 | 0.000 | 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) | 0.002 | 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".