Responding to Intraoperative Neuromonitoring Changes During Pediatric Coronal Spinal Deformity Surgery
Bibliographic record
Abstract
STUDY DESIGN: Retrospective case study on prospectively collected data. OBJECTIVES: The purpose of this explorative study was: 1) to determine if patterns of spinal cord injury could be detected through intra-operative neuromonitoring (IONM) changes in pediatric patients undergoing spinal deformity corrections, 2) to identify if perfusion based or direct trauma causes of IONM changes could be distinguished, 3) to observe the effects of the interventions performed in response to these events, and 4) to attempt to identify different treatment algorithms for the different causes of IONM alerts. METHODS: Prospectively collected neuromonitoring data in pre-established forms on consecutive pediatric patients undergoing coronal spinal deformity surgery at a single center was reviewed. Real-time data was collected on IONM alerts with >50% loss in signal. Patients with alerts were divided into 2 groups: unilateral changes (direct cord trauma), and bilateral MEP changes (cord perfusion deficits). RESULTS: A total of 97 pediatric patients involving 71 females and 26 males with a mean age of 14.9 (11-18) years were included in this study. There were 39 alerts in 27 patients (27.8% overall incidence). All bilateral changes responded to a combination of transfusion, increasing blood pressure, and rod removal. Unilateral changes as a result of direct trauma, mainly during laminotomies for osteotomies, improved with removal of the causative agent. Following corrective actions in response to the alerts, all cases were completed as planned. Signal returned to near baseline in 20/27 patients at closure, with no new neurological deficits in this series. CONCLUSION: A high incidence of alerts occurred in this series of cases. Dividing IONM changes into perfusion-based vs direct trauma directed treatment to the offending cause, allowing for safe corrections of the deformities. Patients did not need to recover IONM signal to baseline to have a normal neurological examination.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".