Intraoperative Electroencephalography Alpha-Band Power Is a Better Proxy for Preoperative Low MoCA Under Propofol Compared With Sevoflurane
Bibliographic record
Abstract
BACKGROUND: Preoperative abnormal cognitive status is a risk factor for postoperative complications yet remains underdiagnosed. During propofol general anesthesia, intraoperative electroencephalography (EEG) variables, such as alpha band power (α-BP), correlate with cognitive status. This relationship under sevoflurane is unclear. We investigated whether EEG biomarkers of poor cognitive status found under propofol could be extended to sevoflurane. METHODS: In this monocentric prospective observational study, 106 patients with intraoperative EEG monitoring were included (propofol/sevoflurane = 55/51). We administered the Montreal Cognitive Assessment (MoCA) scale to identify abnormal cognition (low MoCA) 1 day before intervention. EEG variables included delta to beta frequency band powers. Results were adjusted to age and drug dosage. We assessed depth of anesthesia (DoA) using the spectral edge frequency (SEF 95 ) and maintained it within (8-13) Hz. RESULTS: The difference in α-BP between low and normal MoCA patients was significantly larger among propofol patients (propofol: 4.3 ± 4.8 dB versus sevoflurane: 1.5 ± 3.4 dB, P = .022). SEF 95 and age were not statistically different between sevoflurane and propofol groups. After adjusting to age and dose, low α-BP was significantly associated with low MoCA under propofol (odds ratio [OR] [confidence interval {CI}] = 0.39 [0.16-0.94], P = .034), but not under sevoflurane, where theta-band power was significantly associated with low MoCA (OR [CI] = 0.31 [0.13-0.73], P = .007). CONCLUSIONS: We suggest that intraoperative EEG biomarkers of abnormal cognition differ between propofol and sevoflurane under general anesthesia.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".