Neurocognition after Electroencephalography Guided Anesthetic Induction with Dexmeditomidine in Neurosurgical Patients: A Case Series
Bibliographic record
Abstract
The use of Dexmeditomidine as the primary agent for induction of anesthesia has only been sparingly described due to the concern of quality of anesthetic induction. Although the use of dexmeditomidine has been described for awake craniotomies during the initial phase of sleep-awake-sleep cycle, its use in other forms of neurosurgery has been sparingly described. In this case series we explored the feasibility of combining dexmedetomidine with sevoflurane in an anesthetic induction algorithm. All patients achieved smooth anesthetic induction with introduction of sevoflurane after 8 minutes of dexmeditomidine infusion @ 0.6 mcg/kg/min. The whole induction process was carried out in continuous EEG monitoring using the double banana montage in all patients. Quality of anesthetic induction was evaluated by Viby-Mogensen's criteria and the postoperative cognitive parameters evaluated by Montreal Cognitive Assessment (MoCA) score and Confusion Assessment Method - Short form (CAM-S) score ruled out cognitive decline in this present case series.We conclude that a structured anesthetic induction algorithm incorporating dexmeditomidine with sevoflurane followed by optimal intraoperative hemodynamics and anesthetic maintenance, can enhance patient outcomes and postoperative cognitive function.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".