Spectrum and Predictors of Refractory Status Epilepticus in a Developing Country
Bibliographic record
Abstract
OBJECTIVE: Refractory status epilepticus (RSE) can influence the outcome of status epilepticus (SE). In the present study, we report the aetiology and predictors of outcomes of RSE in a developing country. METHODS: This is a prospective hospital-based study of SE patients (continuous seizures for five minutes or more). Those who had SE persisting after two antiepileptic drugs were defined as having RSE. We present the demographic information, duration, and type of SE, and we note its severity using the status epilepticus severity score (STESS), its aetiology, comorbidities and imaging findings. The outcome of RSE was defined as cessation of seizures and the condition upon discharge, as assessed by the modified Rankin Scale. RESULTS: A total of 35 (42.5%) of our 81 patients had RSE. The median duration of SE before starting treatment was 2 hours (range=0.008-160 h). The most common causes of RSE were stroke in 5 (14.3%), central nervous system (CNS) infections in 12 (34.3%) and metabolic encephalopathies in 13 (37.1%) patients. Some 21 (60%) patients had comorbidities, and the STESS was favourable in 7 (20%) patients. A total of 14 (20%) patients died, but death was directly related to SE in only one of these. Some 10 patients had super-refractory status epilepticus, which was due to CNS infection in 5 (50%) and metabolic encephalopathy in 3 (30%). On multivariate analysis, an unfavourable STESS (p=0.05) and duration of SE before treatment (p=0.01) predicted RSE. Metabolic aetiology (p=0.05), mechanical ventilation (p60 years (p=0.003) were predictors of poor outcomes. CONCLUSIONS: RSE was common (42.5%) among patients with SE in a tertiary care center in India. It was associated with high mortality and poor outcomes. Age above 60 years and metabolic aetiology were found to be predictors of poor 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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".