Intraoperative Stimulation Mapping in Neurosurgery for Anesthesiologists—Part 1: The Technical Nuances
Bibliographic record
Abstract
Abstract Brain mapping has evolved tremendously in the past decade, fueled by advances in functional neuroimaging technology in neuro-oncology and epilepsy surgery. Despite this, wide anatomic-functional interindividual variability and intraoperative brain shift continue to challenge neurosurgeons performing surgery within or near eloquent brain regions. As such, intraoperative direct cortical and subcortical stimulation mapping remains the gold standard for localizing eloquent brain regions with precision for a safe and tailored resection. Intraoperative stimulation mapping (ISM) allows for maximizing the extent of resection while minimizing postoperative neurological deficits, resulting in better patient outcomes. Understanding the technical nuances of ISM is imperative for the anesthesiologist to provide better anesthetic management tailored to the surgery and stimulation mapping planned. A comprehensive search was performed on electronic databases to identify articles describing intraoperative cortical and subcortical mapping, language, and motor mapping. In the first part of this narrative review, we summarize the salient technical aspects of ISM and the common neurophysiological tasks assessed intraoperatively relevant to the anesthesiologist.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".