<scp>EEG</scp> Response to Sedation Interruption Complements Behavioral Assessment After Severe Brain Injury
Bibliographic record
Abstract
OBJECTIVE: Accurate assessment of the level of consciousness and potential to recover in patients with severe brain injury underpins crucial decisions in the intensive care unit but remains a major challenge for the clinical team. The neurological wake-up test is a widely used assessment tool. However, many patients' behavioral responses during a short interruption of sedation are ambiguous or absent, yielding little prognostic value. This study assesses the brain's electroencephalogram response during an interruption of propofol sedation to complement behavioral assessment during the neurological wake-up test to predict survival, recovery of consciousness, and long-term functional outcomes in patients with acute severe brain injury. METHODS: We recorded 128-channel EEG from 41 severely brain-injured patients during a clinically indicated neurological wake-up test. Behavioral assessment was performed before and after interruption of propofol sedation, using the Glasgow Coma Scale. Brain response to sedation interruption was quantified using EEG power, spatial ratios, and the spectral exponent. RESULTS: Recovery of responsiveness during the neurological wake-up test is reflected in participants' brain response to sedation interruption. Electrophysiological patterns can be decoupled from participant behavioral response, with some individuals demonstrating neurophysiological signs of waking up despite an absent behavioral response. Using the brain response to complement behavioral assessment improved prognostic value, distinguished patients according to survival and outperformed outcome predictions of the patients' attending physician. INTERPRETATION: EEG can complement behavioral assessment during the neurological wake-up test 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 | 0.003 |
| 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".