Electroencephalography in Predicting Short-Term Clinical Outcomes after Cardiac Arrest
Bibliographic record
Abstract
BACKGROUND: Early consciousness recovery after cardiac arrest (CA) is one of the most explicit and self-evident prognostic factors for clinical outcomes. We aimed to evaluate the prognostic value of electroencephalography (EEG) phenotypes according to the American Clinical Neurophysiology Society's Critical Care EEG classification for predicting early recovery after CA. METHODS: Consecutive patients admitted to the ICU after CA were enrolled. We analyzed Glasgow Coma Scale (GCS) score within 10 days after CA and evaluated mortality within 28 days according to EEG pattern subtype. RESULTS: Among the total of 71 patients, 9 had periodic discharges (PDs) EEG pattern, 4 had rhythmic delta activity (RDA), 8 had spike-and-wave (SW), 22 had low voltage, 5 had burst suppression, and 23 had other EEG patterns. Initial GCS scores, GCS scores 3 days after CA (or 3 days after targeted temperature management [TTM]), and 10 days after CA (or 10 days after TTM) were significantly different among EEG subtypes (p < 0.001, respectively) (Table 2). GCS scores were significantly higher in RDA and the other EEG group compared to the PDs, SW, low voltage, and burst suppression groups (p < 0.001). Significant group × time interactions were observed for the follow-up period between EEG phenotypes (p < 0.001) demonstrating the most increase in the other EEG pattern group. CONCLUSIONS: Consciousness states were significantly worse in the PDs, SW, burst suppression, and low-voltage groups compared to the RDA and the other EEG pattern within 10 days after CA. The degree of consciousness recovery differed significantly by EEG pattern subtype within 10 days.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".