Cortical Oscillations and Connectivity During Postoperative Recovery
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to test whether postoperative electroencephalographic (EEG) biomarkers, parietal alpha power and frontal-parietal connectivity, were associated with measures of clinical recovery in adult surgical patients. METHODS: This is a secondary analysis of a prospective cohort study that analyzed intraoperative connectivity patterns in adult surgical patients (N=53). Wireless, whole-scalp EEG data were collected in the postanesthesia care unit and assessed for relevance to clinical and neurocognitive recovery. Parietal alpha power and frontal-parietal connectivity (estimated by weighted phase lag index) were tested for associations with postanesthesia care unit discharge readiness and University of Michigan Sedation Scale scores upon postoperative admission. Bivariable correlation and regression models were constructed to test for unadjusted associations, then multivariable regression models were constructed to adjust for confounding. RESULTS: Postoperative EEG patterns were characterized by a predominance of alpha parietal power and frontal-parietal connectivity. Neither relative parietal alpha power (% alpha, -0.25; 95% confidence interval [CI], -1.41 to 0.90; P=0.657) nor alpha frontal-parietal connectivity (weighted phase lag index, -82; 95% CI, -237 to 73; P=0.287) were associated with time until postanesthesia discharge criteria were met. Furthermore, neither alpha power (-0.03; 95% CI, -0.07 to 0.01; P=0.206) nor alpha frontal-parietal connectivity (-4.2; 95% CI, -11 to 2.6; P=0.226) were associated with sedation scores upon initial assessment. CONCLUSIONS: In a pragmatic study investigating clinically relevant endpoints of postoperative recovery, we found no correlation with surrogate measures of brain neurodynamics. These data contribute to the overall impetus of developing anesthetic-invariant and generalizable markers of brain recovery.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".