Utility of intraoperative neurophysiological monitoring in detecting motor and sensory nerve injuries in pediatric high-grade spondylolisthesis
Bibliographic record
Abstract
BACKGROUND CONTEXT: Intraoperative neuromonitoring (IONM) during surgical correction of spinal deformity has been shown to reduce iatrogenic injury in pediatric and adult populations. Although motor-evoked potentials (MEP), somatosensory-evoked potentials (SSEP), and electromyography (EMG) have been shown to be highly sensitive and specific in detecting spinal cord and nerve root injuries, their utility in detecting motor and sensory nerve root injury in pediatric high-grade spondylolisthesis (HGS) remains unknown. PURPOSE: We aim to assess the diagnostic accuracy and therapeutic impact of unimodal and multimodal IONM in the surgical management of HGS. STUDY DESIGN/SETTING: Retrospective cohort study. PATIENT SAMPLE: Pediatric patients undergoing posterior spinal fusion (PSF) for treatment of HGS. OUTCOME MEASURES: Data on patient demographics, spinopelvic and spondylolisthesis parameters, and the presence of pre-and postoperative neurological deficits were collected. METHODS: Intraoperative MEP, SSEP, and EMG alerts were recorded. Alert criteria were defined as a change in amplitude of more than 50% for MEP and/or SSEP, with or without change in latency, and more than 10 seconds of sustained EMG activity. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated for each modality and the combination of MEP and SSEP. The 95% confidence intervals (CIs) were calculated using the exact (Clopper-Pearson) method. RESULTS: Fifty-four pediatric patients with HGS undergoing PSF between 2003 and 2021 in a single tertiary center were included. Seventy-two percent (39/54) of patients were female; the average age of patients was 13.7±2.3 years. The sensitivity of MEP in detecting new postoperative neurologic deficit was 92.3% (95% CI [64.0-99.8]), SSEP 77.8% (95% CI [40.0-97.2]), EMG 69.2% (95% CI [38.6-90.9]), and combination MEP and SSEP 100% (95% CI [73.5-100]). The specificity of MEP was 80.0% (95% CI [64.4-91.0]), SSEP 95.1% (95% CI [83.5-99.4]), EMG 65.9% (95% CI [49.4-79.9]), and combination MEP and SSEP 82.9% (95% CI [67.9-92.9]). The accuracy of SSEP was 92.0% (95% CI [80.8%-97.8%]), and the combination of MEP and SSEP was 86.8% (95% CI [74.7%-94.5%]). Twelve (22.2%) patients had a new motor or sensory deficit diagnosed immediately postoperatively. Nine patients made a full recovery, and 3 had some neurologic deficit on final follow-up. CONCLUSION: Unimodal IONM using SSEP and MEP alone were accurate in diagnosing sensory and motor nerve root injuries, respectively. The diagnostic accuracy in predicting motor and sensory nerve injuries in pediatric HGS improved further with the use of multimodal IONM (combining MEP and SEP). We recommend the utilization of multimodal IONM in all HGS PSF surgeries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".