Association of sleep and anaesthesia <scp>EEG</scp> biomarkers with preoperative <scp>MoCA</scp> score: A pilot study
Bibliographic record
Abstract
Abstract Introduction Preoperative cognitive impairments increase the risk of postoperative complications. The electroencephalogram (EEG) could provide information on cognitive vulnerability. The feasibility and clinical relevance of sleep EEG (EEGsleep) compared to intraoperative EEG (EEGintraop) in cognitive risk stratification remains to be explored. We investigated similarities between EEGsleep and EEGintraop vis‐a‐vis preoperative cognitive impairments. Methods Pilot study including 27 patients (63 year old [53.5, 70.0]) to whom Montreal cognitive assessment (MoCA) and EEGsleep were administered 1 day before a propofol‐based general anaesthesia, in addition to EEGintraop acquisition from depth‐of‐anaesthesia monitors. Sleep spindles on EEGsleep and intraoperative alpha‐band power on EEGintraop were particularly explored. Results In total, 11 (41%) patients had a MoCA <25 points. These patients had a significantly lower sleep spindle power on EEGsleep (25 vs. 40 μv2/Hz, p = .035) and had a weaker intraoperative alpha‐band power on EEGintraop (85 vs. 150 μv2/Hz, p = .001) compared to patients with normal MoCA. Correlation between sleep spindle and intraoperative alpha‐band power was positive and significant (r = 0.544, p = .003). Conclusion Preoperative cognitive impairment appears to be detectable by both EEGsleep and EEGintraop. Preoperative sleep EEG to assess perioperative cognitive risk is feasible but more data are needed to demonstrate its benefit compared to intraoperative EEG.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".