Effects of Adjunct Analgesics and Novel Anesthetic Agents on Intraoperative Neuromonitoring: A Scoping Review
Bibliographic record
Abstract
Intraoperative neuromonitoring (IONM) plays a critical role in assessing neural integrity and guiding surgical decision-making. The effects of traditional anesthetic agents on IONM are well-established, though the impact of novel anesthetics and adjunct analgesics remains unclear. This scoping review aims to evaluate the effects of novel anesthetic and adjunct analgesic agents on somatosensory evoked potentials (SSEPs) and motor evoked potentials (MEPs) in the intraoperative setting. A comprehensive literature search was conducted using Medline, Cochrane Central, CINAHL, Scopus, LILACS, and Embase, from inception to February 2025. Randomized controlled trials, observational studies, and case series assessing the effects of lidocaine, ketamine, dexmedetomidine, methadone, magnesium, gabapentinoids, xenon, and remimazolam on IONM were included in the review. Backward citation searching was also performed on the included studies. A total of 53 studies met inclusion criteria, comprising 30 randomized and 23 nonrandomized studies. Lidocaine, when administered within analgesic dosing, had minimal impact on SSEPs and MEPs. Ketamine exhibited augmentative, neutral, or suppressive effects on IONM, which appeared to be dependent on the dosing regimen. Dexmedetomidine demonstrated mixed effects on IONM, potentially due to dose-dependent hemodynamic alterations and its unique pharmacokinetic properties. Methadone and magnesium showed minimal impact on IONM, while xenon was associated with clinically relevant suppression of evoked potentials. Remimazolam appeared to maintain neuromonitoring integrity at clinically relevant doses. The effects of novel anesthetic and adjunct analgesic agents on IONM are variable and dose-dependent, necessitating individualized anesthetic strategies. Future research should focus on larger randomized trials with standardized protocols to better define their roles in a neuromonitoring-compatible anesthetic regimen.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".