Anesthesia for Functional Neurosurgery
Bibliographic record
Abstract
The use of functional stereotactic neurosurgery is increasing for treatment of patients with movement disorders and other chronic illnesses. The anesthetic considerations include the influence of the anesthetic agents on the microelectrode recordings and stimulation testing of an awake patient. The purpose of this study was to review the anesthetic management and incidences of intraoperative complications during functional neurosurgery in our institution. One hundred seventy-eight patients underwent an ablative procedure (n = 6) or the insertion of deep brain stimulator (n = 172) under monitored anesthesia care for movement disorders (n = 124), chronic pain (n = 20), and other procedures (n = 34). Local anesthetic was used for head frame pin sites and burr holes. No sedation/analgesia was administered to 57 (32%) patients. One patient required conscious sedation and another general anesthesia for the entire procedure. The remainder received small increments (mean +/- SD) of propofol (113 +/- 73 mg), midazolam (1.6 +/- 0.8 mg), and/or fentanyl (93 +/- 55 mug). Intraoperative complications that occurred in 16% of the patients included seizures (n = 8), change in neurologic status (n = 5), airway obstruction (n = 2), and hypertension (n = 7). Functional neurosurgery can be performed with minimal anesthesia in many patients. Awareness and vigilance can improve the identification and early treatment of intraoperative complications such as seizures, loss of airway, and changes in the neurologic status.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.024 |
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".