2029 Impact of Intraoperative Neurophysiological Monitoring on Surgical Outcomes in Patients With Intradural Extramedullary Spinal Cord Tumors – A Systematic Review of the Literature
Bibliographic record
Abstract
INTRODUCTION: Intraoperative neurophysiologic monitoring (IONM) plays an important role in the prevention of neurological complications following spinal cord surgeries. There is a paucity of systematic reviews on the surgical outcomes of IONM modalities in patients with intradural extramedullary (IDEM) tumors of the spinal cord. METHODS: The authors searched a total of five electronic databases in May 2024 to retrieve the relevant studies. The eligible studies included patients diagnosed with IDEM, discussed the surgical outcomes of IONM and the predictive performance of the modalities for postoperative neurological deficits, and were published in the English language. The quality assessment was performed using the Newcastle-Ottawa Scale for retrospective and prospective cohort studies. RESULTS: The review included ten studies with a total of 1323 patients who underwent spinal cord surgery for IDEM tumors. There were 587 (44%) male participants, and 736 female participants (56%). Overall, IONM demonstrated superior benefits, reducing complications (p < 0.05), preserving neurological function (p < 0.05), greater quality of life, radiological measures, and lower complication rates. Long-term follow-up showed lower recurrence rates and higher overall survival among IONM patients. CONCLUSIONS: This review underscores the substantial benefits of intraoperative neurophysiologic monitoring IONM in IDEM spinal cord tumor surgery. IONM reduces neurological complications, preserves function, and enhances surgical success and overall patient outcomes. Integrating IONM into spinal cord surgery is essential for optimizing patient safety and improving long-term prognosis.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".