A Descriptive Analysis of Prognostic Indicators in Patients with Non-Convulsive Status Epilepticus in a Tertiary Hospital Population
Bibliographic record
Abstract
Background: Non-convulsive status epilepticus (NCSE) is defined as a change in mental state of at least 30 minutes associated with continuous or nearly continuous epileptiform discharges. Identification of prognostic indicators can guide decision making surrounding the use of poorly established treatment interventions in this heterogeneous population. Methods: We identified 66 consecutive inpatients with NCSE. Data surrounding clinical, electrographic, and treatment factors were collected via a retrospective systematic review of medical records and electronic EEGs, and were correlated with discharge outcome (return to baseline, new disability, or death). Results: Of all subjects, 21% returned to baseline, 26% acquired new disability, and 53% died, of whom half had anoxic encephalopathy. On univariate analysis, seventeen variables correlated significantly with death, although multivariate logistic regression analysis subsequently identified only comatose state and number of life threatening comorbidities as independent predictors of mortality. Of survivors, comatose state, critical care environment, length of hospital stay, and acute symptomatic seizures predicted new disability, with the latter two showing independent significance. Following exclusion of cases with anoxic encephalopathy, the use of an anaesthetic infusion was also an independent predictor of mortality. Conclusions: NCSE is associated with variable morbidity and mortality. While one fifth of our NCSE patients returned to baseline, those comatose with acute structural/metabolic seizures, anaesthetic infusions, and life threatening comorbidities were unlikely to survive without disability at discharge.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".