Electroencephalography (EEG) for Neurological Prognostication in Post-Anoxic Coma Following Cardiac Arrest and Its Relationship to Outcome
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Cardiac arrest may cause significant hypoxic-ischemic injury leading to coma, seizures, myoclonic jerks, or status epilepticus. Mortality is high, but accurate prognostication is challenging. A multimodal approach is employed, in which electroencephalography (EEG) forms a key part with several recognised patterns of prognostic significance. METHODS: In this retrospective study, clinical and qualitative features of the EEG of patients admitted to the Intensive Care Unit (ICU) at Kingston General Hospital following cardiac arrest from 2017 to 2020 were reviewed. The study included 81 adult patients (≥18 years). Outcome was assessed using the Cerebral Performance Category (CPC) as 1-2 (favourable) or 3-5 (unfavourable). EEG patterns were divided into groups within the highly malignant, malignant and benign patterns described in the literature. RESULTS: There were a wide range of causes and 22% had a favourable outcome. Highly malignant, malignant and benign patterns were associated with survival in 0%, 70% and 100%, respectively, and favourable outcomes in 0%, 48% and 100%. All patients with seizures died, and 94% with myoclonus had unfavourable outcomes. In contrast, EEG reactivity and improvement on follow-up EEG were associated with a favourable outcome. CONCLUSIONS: Highly malignant EEG, seizures and myoclonus were associated with unfavourable outcomes, while patients with malignant EEG had better outcomes.
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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 | 0.001 |
| 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) | 0.001 | 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".