P.091 Parameterized short-segment EEG improves neurological recovery prediction in patients with severe brain injury
Bibliographic record
Abstract
Background: Predicting neurological recovery in patients with severe brain injury remains challenging. Continuous EEG monitoring can detect malignant patterns but is resource-intensive, and its role in long-term functional outcome prediction is unclear. This study evaluates the utility of parameterized short-segment EEG, acquired via EEG cap, in predicting neurological recovery. Methods: We analyzed short-segment high-density EEGs from 42 patients in the NET-ICU cohort with acute neurological injury. EEGs were pre-processed into standard clinical formats and parameterized using five visual EEG features associated with outcome prediction. Random Forest Classifier (RFC) models were trained and cross-validated to predict recovery of responsiveness (following 1-2 step commands during or after ICU admission) using: EEG features alone; clinician prediction combined with EEG features. Results: EEG-based prediction outperformed clinician bedside assessment (AUC ROC: 0.80 vs. 0.67) under the RFC model. Combining clinician Glasgow Outcome Scale–Extended (GOSE) scores with EEG features improved overall predictive performance (AUC ROC: 0.91). Conclusions: Standardized EEG features obtained using EEG caps can improve the accuracy of neurological recovery predictions in patients with acute severe brain injury. This suggests that automated extraction of background brain signals has the potential to provide clinically meaningful prognostic information in critical care settings, enhancing accessibility and resource efficiency.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".