CORE-Coma: Deep Learning Framework for Coma Prognosis fromAuditory Event-Related Potentials
Bibliographic record
Abstract
Accurate prognosis of coma emergence is difficult because bedside behavioral scales can fail to detect residual consciousness. Auditory oddball event-related potentials (ERPs) offer a physiological readout, but single-component markers (e.g., MMN or P3) have limited sensitivity and generalizability. We present CORE-Coma, a deep learning framework for full-waveform ERP analysis, trained exclusively on healthy controls and evaluated zero-shot in coma patients. We analyzed ERPs from 39 healthy controls and 8 coma patients in the intensive care unit (ICU), segmenting EEG recordings into ~5-minute sub-blocks to capture temporal fluctuations. We define two complementary, model-derived metrics: a time-resolved ERP Separability Score (ESS) and a subject-level Global ERP Separability Index (GESI). Controls showed near-ceiling standard–deviant separability (ROC AUC=0.99), while separability was reduced in coma (ROC AUC=0.68). CORE-Coma identified all patients who emerged from coma (3/3; sensitivity 100%) and 4/5 patients who did not emerge (specificity 80%), yielding accuracy=87.5% (7/8). ESS revealed temporal fluctuations (waxing–waning) of responsiveness in coma at ~5-minute resolution, absent in controls. SHAP explanations localized influential features, including frontocentral electrodes and time windows consistent with canonical oddball components: 100–150 ms (N1/MMN) and 270–370 ms (P3a/P3b). By combining bedside-feasible scalp EEG with time-resolved and subject-level metrics, CORE-Coma offers an etiology-agnostic approach to coma prognosis. Prospective multicenter studies are needed to validate performance and support clinical deployment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".