Intraoperative Stimulation Mapping in Neurosurgery for Anesthesiologists, Part 2: The Anesthetic Considerations
Bibliographic record
Abstract
Abstract Intraoperative language and sensorimotor function mapping with direct electrical stimulation allows precise identification of functionally important brain regions. Direct electrical stimulation brain mapping has become the standard of care for the resection of brain lesions near or within eloquent regions with various patient outcome benefits. Intraoperative stimulation mapping (ISM) is commonly performed in an awake patient for language and motor assessments. However, motor mapping under general anesthesia, termed asleep motor mapping, has been increasingly performed over the last two decades for lesions primarily affecting the motor areas of the brain. Both asleep-awake-asleep and monitored anesthesia care have been successfully used for awake craniotomy in modern neuroanesthesia. Each anesthetic agent exerts varying effects on the quality of ISM, especially under general anesthesia. Careful selection of an anesthetic technique is crucial for the successful performance of ISM in both awake and asleep conditions. A comprehensive search was performed on electronic databases such as PubMed, Embase, Cochrane, Scopus, Web of Science, and Google Scholar to identify articles describing anesthesia for awake craniotomy, intraoperative brain mapping, and asleep motor mapping. In the second part of this narrative review, we summarize the effects of different anesthetic regimes and agents on ISM, causes of the failure of awake craniotomy and mapping, and outline the anesthetic considerations for ISM during awake craniotomy and asleep motor mapping.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".