The Evidence for Intraoperative Neurophysiological Monitoring in Spine Surgery
Bibliographic record
Abstract
OBJECTIVE: The objective of this article was to undertake a systematic review of the literature to determine whether IOM is able to sensitively and specifically detect intraoperative neurologic injury during spine surgery and to assess whether IOM results in improved outcomes for patients during these procedures. SUMMARY AND BACKGROUND DATA: Although relatively uncommon, perioperative neurologic injury, in particular spinal cord injury, is one of the most feared complications of spinal surgery. Intraoperative neuromonitoring (IOM) has been proposed as a method which could reduce perioperative neurologic complications after spine surgery. METHODS: A systematic review of the English language literature was undertaken for articles published between 1990 and March 2009. MEDLINE, EMBASE, and Cochrane Collaborative Library databases were searched, as were the reference lists of published articles examining the use of IOM in spine surgery. Two independent reviewers assessed the level of evidence quality using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) criteria, and disagreements were resolved by consensus. RESULTS: A total of 103 articles were initially screened and 32 ultimately met the predetermined inclusion criteria. We determined that there is a high level of evidence that multimodal IOM is sensitive and specific for detecting intraoperative neurologic injury during spine surgery. There is a low level of evidence that IOM reduces the rate of new or worsened perioperative neurologic deficits. There is very low evidence that an intraoperative response to a neuromonitoring alert reduces the rate of perioperative neurologic deterioration. CONCLUSION: Based on strong evidence that multimodality intraoperative neuromonitoring (MIOM) is sensitive and specific for detecting intraoperative neurologic injury during spine surgery, it is recommended that the use of MIOM be considered in spine surgery where the spinal cord or nerve roots are deemed to be at risk, including procedures involving deformity correction and procedures that require the placement of instrumentation. There is a need to develop evidence-based protocols to deal with intraoperative changes in MIOM and to validate these prospectively.
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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 | 0.069 | 0.323 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.020 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".