The influence of anaesthesia on intraoperative neuromonitoring changes in high-risk spinal surgery
Bibliographic record
Abstract
Abstract Background: The use of intraoperative neuromonitoring is a well-established method of detecting neurologic injuries during spine surgery. Anaesthesia, especially inhalational agents, influence motor evoked potential (MEP) monitoring. The aim of our study was to compare the effect of balanced anaesthesia (BA) (intravenous plus inhalational anaesthesia) and total intravenous anaesthesia (TIVA) on the incidence of intraoperative neuromonitoring changes, interventions performed and neurological outcomes of patients following high-risk spinal surgery. Methods: After Research and Ethics Board approval, a retrospective review of 155 patients who underwent spinal surgery with MEP was performed. Data were collected on changes in MEP and/or somatosensory evoked potential, interventions performed and neurological outcomes. Patients were divided into BA and TIVA groups and data were analysed. Results: A total of 152 patients were eligible for the study (mean age 54 ± 17, male: female 45:55). A BA technique was used in 62% and TIVA in 38%. Desflurane (<0.5 minimum alveolar concentration [MAC]) was used in 85% BA cases. Intraoperative neuromonitoring changes occurred in 11.8% (18/152) of cases. There was no statistical difference in the incidence of monitoring changes between BA (78%) and TIVA (22%) groups (P = 0.197). Anaesthetic or surgical interventions were performed in 12 patients, with a resolution of changes in 50% (P = 0.455). All 5 patients with persistent MEP changes had worsening of existing neurological deficits postoperatively; 8 had transient MEP changes, and 2 experienced worsening of existing neurological deficits. Conclusions: We found that intraoperative neurophysiological monitoring can be performed with both BA (MAC <0.5) and TIVA in high-risk spinal surgery with no statistical difference in the incidence of intraoperative monitoring changes.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".