Identifying Suspected Volume Conduction Contamination of External Anal Sphincter Motor Evoked Potentials in Lumbosacral Spine Surgery
Bibliographic record
Abstract
PURPOSE: Iatrogenic injury to sacral nerve roots poses significant quality of life issues for patients. Motor evoked potential (MEP) monitoring can be used for intraoperative surveillance of these important structures. We hypothesized that volume conducted depolarizations from gluteus maximus (GM) may contaminate external anal sphincter (EAS) MEP results during lumbosacral spine surgery. METHODS: Motor evoked potential from the EAS and medial GM in 40 patients were prospectively assessed for inter-muscle volume conduction during lumbosacral spine surgeries. Peak latency matching between the EAS and GM MEP recordings conditionally identified volume conduction (VC+) or no volume conduction (VC-). Linear regression and power spectral density analysis of EAS and medial GM MEP amplitudes were performed from VC+ and VC- data pairs to confirm intermuscle electrical cross-talk. RESULTS: Motor evoked potential peak latency matching identified putative VC+ in 9 of 40 patients (22.5%). Mean regression coefficients (r2) from peak-to-peak EAS and medial GM MEP amplitude plots were 0.83 ± 0.04 for VC+ and 0.34 ± 0.06 for VC- MEP (P < 0.001). Power spectral density analysis identified the major frequency component in the MEP responses. The mean frequency difference between VC+ EAS and medial GM MEP responses were 0.4 ± 0.2 Hz compared with 3.5 ± 0.6 Hz for VC- MEP (P < 0.001). CONCLUSIONS: Our data support using peak latency matching between EAS and GM MEP to identify spurious MEP results because of intermuscle volume conduction. Neuromonitorists should be aware of this possible cross-muscle conflict to avoid interpretation errors during lumbosacral procedures using EAS MEP.
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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) | 0.001 | 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".