Temporal Summation of Subthreshold Stimuli in Human Motor Axons: Implications for Intraoperative Neuromonitoring
Bibliographic record
Abstract
Abstract Objectives To examine how stimulus amplitude and width influence subthreshold superexcitability of peripheral axons and to provide evidence-based recommendations for minimizing inadvertent compound muscle action potential (CMAP) generation during intraoperative corticobulbar monitoring. Methods Fifteen healthy participants received median nerve stimulation under nine conditions combining three amplitudes (80%, 85%, and 90% of threshold) and three pulse widths (0.1 ms, 0.5 ms, 1.0 ms). Trains of 1–7 subthreshold pulses (2 ms interpulse interval) were delivered 10 times per condition. CMAPs were recorded from the abductor pollicis brevis (APB), and the probability of a response exceeding 100 µV (baseline-to-negative-peak) was calculated. Persistent sodium current was estimated using the latent addition test (LAh). Results Higher pulse amplitudes and wider pulse widths significantly increased CMAP probability, with a significant interaction (F(4,56) = 4.853, p = .002, partial η² = .257). All pairwise comparisons were significant (p ≤ .023). When controlling for rheobase, LAh was positively correlated with response probability (r partial (12) = .539, p = .047). Conclusions & Significance Subthreshold trains activate motor axons in a predictable manner depending on amplitude, width, and train length. These findings challenge current IONM assumptions and highlight the need for threshold-referenced, standardized protocols.
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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 | 0.004 |
| 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) | 0.001 | 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".