Epileptiform Activity in Neurocritical Care Patients
Bibliographic record
Abstract
BACKGROUND: Non-convulsive seizures have been reported to be common in neurocritical care patients. Many jurisdictions do not have sufficient resources to enable routine continuous electroencephalography (cEEG) and instead use primarily intermittent EEG, for which the diagnostic yield remains uncertain. Determining risk factors for epileptiform activity and seizures could help identify patients who might particularly benefit from EEG monitoring. METHODS: We performed a cohort study involving neurocritical care patients with admission Glascow Coma Scale (GCS) scores ≤ 12, who underwent ≥ 1 EEG. EEGs were reviewed for presence of interictal discharges, periodic epileptiform discharges (PEDs), and seizures. Multivariate analysis was used to identify predictors of these findings and to describe their prognostic implications. RESULTS: 393 patients met inclusion criteria. 34 underwent cEEG, usually because epileptiform activity was first detected on a routine EEG. The prevalence of PEDs or electrographic seizures was 13%, and was highest with anoxic encephalopathy and central nervous system infections. Other independent predictors for epileptiform activity included a history of convulsive seizure(s), increasing age, deeper coma, and female gender. Although patients with epileptiform activity had higher mortality, this association disappeared after adjustment for confounders. CONCLUSION: Approximately 7-8 neurocritical care patients must undergo intermittent EEG monitoring in order to diagnose one with PEDs or seizures. The predictors we identified could potentially help guide use of resources. Repeated intermittent studies, or cEEG, should be considered in patients with multiple risk factors, or when interictal discharges are identified on an initial EEG. It remains unclear whether aggressive prevention and treatment of electrographic seizures improves neurologic 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".