Diagnostic Accuracy of Neuromonitoring for Identification of New Neurologic Deficits in Pediatric Spinal Fusion Surgery
Bibliographic record
Abstract
BACKGROUND: Intraoperative neuromonitoring (IONM) modalities, transcranial motor-evoked potentials (TcMEPs), and somatosensory-evoked potentials (SSEPs) are accepted methods to identify impending spinal cord injury during spinal fusion surgery. Debate exists over sensitivity and specificity of these modalities. Our purpose was to measure the incidence of new neurologic deficits (NNDs) and estimate sensitivity and specificity of IONM modalities. METHODS: Institutional Ethics Board approval was obtained to review charts of patients younger than 22 years undergoing scoliosis surgery from 2007 to 2014 retrospectively. The definition of true-positive patients included two subgroups: (1) patients with an IONM alert, which did not resolve despite the interventions and had a NND postoperatively; or (2) patients with an IONM alert triggering interventions and the alert resolved with no NND postoperatively. Subgroup 2 of the definition is debatable; thus, we performed a multiple sensitivity analysis with three assumptions. Assumption 1: without interventions, all such patients would have experienced NNDs (assumption used in previous studies); Assumption 2: without intervention, half of these patients would have experienced NNDs; Assumption 3: without intervention, none of these of patients would have experienced NNDs. RESULTS: We included 296 patients. Patients with incomplete charts (n = 3), no IONM monitoring (n = 11), and inadequate baseline IONM (n = 7) were excluded. The incidence of NND was 3.7% (95% confidence interval, 2.1%-6.5%). Successful IONM in at least one modality was obtained in 275 patients (92.9%), of whom 268 (97.5%) and 259 (94.2%) had successful baseline TcMEP or SSEP signals, respectively. Fifty-one (17%) patients had IONM alerts, 41 were only TcMEP, 5 were only SSEP, and 5 were in both modalities. After interventions, 42 (82%) patients recovered, 41 had no NND (true-positive under Assumption (1), but one developed a NND (false-negative). Of the 9 patients with no alert recovery, 6 had a NND (true-positive) and 3 did not (false-positives). Of the remaining 224 patients with no alerts, 221 had no NND (true-negatives) and 3 did (false-negatives). Sensitivity was estimated to be 93.5%, 92.2%, and 46.7% for TcMEPs, combination (either TcMEPs or SSEPs), and SSEPs, respectively. Multiple sensitivity analysis demonstrated that sensitivity and specificity vary markedly with different assumptions. CONCLUSION: TcMEPs are more sensitive than SSEP at detecting an impending NND. IONM modalities are highly specific. Both sensitivity and specificity are impacted substantially by assumptions of the impact of interventions on alerts and NND. Properly designed, controlled, multicenter studies are required to establish diagnostic accuracy of IONM in scoliosis surgery.
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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 | 0.017 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".