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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".