EEG response during sedation interruption complements behavioral assessment following severe brain injury
Bibliographic record
Abstract
Abstract Background and Objectives Accurate assessment of level of consciousness and potential to recover in severe brain injury patients underpins crucial decisions in the intensive care unit but remains a major challenge for the clinical team. The neurological wake-up test (NWT) is a widely used assessment tool, but many patients’ behavioral response during a short interruption of sedation is ambiguous or absent, with little prognostic value. This study assesses the brain’s electroencephalogram response during an interruption of propofol sedation to complement behavioral assessment during the NWT to predict survival, recovery of consciousness, and long-term functional outcome in acute severe brain injury patients. Methods We recorded 128-channel EEG of 41 severely brain-injured patients during a clinically indicated NWT. The Glasgow Coma Scale (GCS) was used to assess behavioral responsiveness before and after interruption of sedation (GCS observed ). During the NWT, nine patients regained responsiveness, 13 patients showed ambiguous responsiveness and 19 patients were not responsive. Brain response to sedation interruption was quantified using EEG power, spatial ratios and the spectral exponent. We trained a linear regression model to identify brain patterns related to regaining behavioral responsiveness. We then applied this model to patients whose behavioral responses were ambiguous or absent, using their NWT brain responses to predict a change in behavioral response (ΔGCS predicted ). Prognostic value of the ΔGCS predicted was assessed using the Mann-Whitney-U test and group-separability. The patients’ survival, recovery of responsiveness, and functional outcomes were assessed up to 12 months post-recording. Results EEG patterns during interruption of sedation reliably predicted the GCS observed in patients who regained responsiveness during the NWT. Electrophysiological patterns of waking-up were observed in some patients whose behavioral response was ambiguous or absent. Compared to the GCS observed , the ΔGCS predicted improved separability of prognostic groups and significantly distinguished patients according to survival (U = 87, p<0.05). The EEG-trained model outperformed outcome predictions of the patients’ attending physician and predictions based on the patients’ APACHE score. Discussion EEG can complement behavioral assessment during the NWT to improve prognostication, inform clinicians, family members and caregivers, and to set realistic goals for treatment and therapy.
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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 | 0.002 |
| 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".