A Prospective Study of the Intra- and Postoperative Efficacy of Intraoperative Neuromonitoring in Spinal Cord Stimulation
Bibliographic record
Abstract
BACKGROUND: Accurate lead placement is critical for spinal cord stimulation (SCS) efficacy. The traditional gold standard of awake placement is often technically difficult. While there is retrospective evidence supporting the use of intraoperative neurophysiological monitoring (IOM) as an alternative, a prospective assessment has not yet been performed. OBJECTIVE: To prospectively evaluate pain and functionality outcomes for IOM-guided SCS, validate two IOM modalities as a means to lateralize lead placement and assess whether IOM can be useful for postoperative programming. METHODS: A total of 73 patients were implanted with SCS using electromyography (EMG) and somatosensory-evoked potential collision studies (SSEP-CS) to verify lead placement. Patient pain and function were assessed through serial administration of several validated questionnaires. Stimulation parameters at 6 months were documented. RESULTS: Statistically significant (p < 0.05) improvements were observed in the McGill Pain Questionnaire, Oswestry Disability Index, Pain Catastrophizing Scale, and Visual Analog Scale. EMG and SSEP-CS appropriately lateralized leads in 65/73 (89.0%) and 40/58 (69.0%) cases, respectively. EMG predicted active contacts in use at follow-up with 82.7% sensitivity. CONCLUSIONS: We provide prospective evidence that IOM can be used to verify SCS placement. Additionally, EMG may help to streamline device programming and thereby improve outcomes by predicting the ideal stimulation contacts in many cases.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".