Does Spinal Cord Type Predict Intraoperative Neuro-Monitoring Alerts in Scoliosis Correction Surgery? A Systematic Review and Meta-Analysis of Operative and Radiologic Predictors
Bibliographic record
Abstract
Study Design Systematic literature review and meta-analysis. Objectives Predicting patient risk of intraoperative neuromonitoring (IONM) alerts preoperatively can aid patient counselling and surgical planning. Sielatycki et al established an axial-MRI-based spinal cord classification system to predict risk of IONM alerts in scoliosis correction surgery. We aim to systematically review the literature on operative and radiologic factors associated with IONM alerts, including a novel spinal cord classification. Methods A systematic review and meta-analysis was performed as per the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) Guidelines. A literature search identifying all observational studies comparing patients with and without IONM alerts was conducted. Suitable studies were included. Patient demographics, radiological measures and operative factors were collected. Results 11 studies were included including 3040 patients. Relative to type 3 cords, type 1 (OR = .03, CI = .01-.08, P < .00001), type 2 (OR = .08, CI = .03, P <.00001) and all non-type 3 cords (OR = .05, CI = .02-.16, P < .00001) were associated with significantly lower odds of IONM alerts. Significant radiographic measures for IONM alerts included coronal Cobb angle (MD = 10.66, CI = 5.77-15.56, P < .00001), sagittal Cobb angle (MD = 9.27, CI = 3.28-14.73, P = .0009), sagittal deformity angle ratio (SDAR) (MD = 2.76, CI = 1.57-3.96, P < .00001) and total deformity angle ratio (TDAR) (MD = 3.44, CI = 2.27-4.462, P < .00001). Clinically, estimated blood loss (MD = 274.13, CI = −240.03-788.28, P = .30), operation duration (MD = 50.79, CI = 20.58-81.00, P = .0010), number of levels fused (MD = .92, CI = .43-1.41, P = .0002) and number of vertebral levels resected (MD = .43, CI = .01-.84, P = .05) were significantly greater in IONM alert patients. Conclusions This study highlights the relationship of operative and radiologic factors with IONM alerts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".