Abstract TP426: Impact of Electrographic Seizure Activity on Outcomes Among Critically-ill Patients with Stroke
Bibliographic record
Abstract
Introduction: Patients with severe acute stroke can suffer secondary neurological injury due to a variety of causes. The clinical impact of electrographic seizures among critically-ill stroke patients remains unclear. Methods: We retrospectively analyzed a cohort of consecutive stroke patients admitted to the Neuroscience Intensive Care Unit (NSICU) at the Mount Sinai Hospital who underwent ≥24 hours of continuous video EEG (cEEG) monitoring for alteration in level of consciousness or suspected non-convulsive seizures between May 2013 and June 2015. Results: Of 106 consecutive severe stroke patients admitted to the NSICU who underwent cEEG monitoring, 15 (14%) had clinical or electrographic seizures. The median duration of cEEG monitoring was 1.2 days. Observed seizures included purely electrographic seizures with no clinical correlate in 53%, subtle twitching or altered mental status in 33%, and generalized tonic-clonic seizures with an EEG correlate in 13%. Generalized or lateralized periodic discharges (PEDs) were present in 93% (14/15) of patients in the seizure group, compared to 20% (18/89) in the non-seizure group (P= 0.00). The survival rate at discharge was 53% in the seizure group compared to 73% in the non-seizure group (p = 0.12). Mean ICU length of stay was 16 days in both groups. Of the 11 survivors in the seizure group 100% were severely disabled at discharge (mRS 4 or 5) compared to 75% of the 65 survivors in the non-seizure group.(P=0.06). Conclusion: Critically-ill stroke patients with clinical or electrographic seizures in the ICU tend to have higher mortality and worse functional outcome at the time of discharge. PEDS are an important marker of patients at high risk for ictal activity. Careful identification of acute stroke patients that might benefit from seizure prophylaxis is warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".