Optimal Intraoperative Somatosensory Evoked Potential Stimulus Intensity Can Be Determined by Nerve Action Potential Amplitude
Bibliographic record
Abstract
PURPOSE: Muscle twitch threshold has been used to determine optimal stimulus intensity for somatosensory evoked potentials but neuromuscular blockade precludes the use of muscle twitch during surgery. Accordingly, nerve action potential (NAP) amplitude was investigated as a surrogate to muscle twitch. METHODS: The ulnar and tibial nerves were stimulated at the wrist and ankle, respectively, in 27 patients undergoing spine and brain surgery. After neuromuscular blockade was gone, the stimulus intensity for just maximal NAP amplitude recorded from Erb's point and the popliteal fossa was compared with the stimulus intensity for hypothenar and plantar foot muscle twitch threshold (times two), respectively (Wilcoxon matched pairs test). RESULTS: There was no significant difference between stimulus intensity for just maximal Erb's point and popliteal fossa NAP amplitude when compared with stimulus intensity for hypothenar and plantar foot twitch threshold (times two), respectively. Eight patients required more than twitch intensity (times two) to obtain maximum NAP. CONCLUSIONS: The NAP amplitude may be used to determine optimal somatosensory evoked potential stimulus intensity when muscle twitch is not visible. This method should improve the success of intraoperative somatosensory evoked potential monitoring and decrease erroneous interpretation.
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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 | 0.005 |
| 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".