Convulsive Status Epilepticus in a Cohort of Patients from a Peruvian Academic Hospital
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Status epilepticus is a neurologic emergency whose epidemiology, etiology and management are scarcely known in developing countries. Our objective was to describe the demographic and clinical features as well as the management of generalized convulsive status epilepticus (GCSE) in adult patients admitted to the emergency department of an academic hospital in Peru, between March 2019 and March 2020. METHODS: Observational study of a prospective cohort in which patients were assessed by the emergency and neurology department on the first day of hospitalization, at discharge and at 30 days post-discharge in a follow-up visit. Relevant demographics and clinical data were collected. After being encoded and sorted, univariate statistical analysis was carried out. RESULTS: Of the sample of 59 patients, 62.7% were males, 57.6% were unemployed, 89.8% did not finish high school, and 55.9% had intermittent GCSE with no seizure at arrival. The total calculated median times were: 60 minutes from GCSE onset to hospital arrival, 110 minutes from GCSE onset to 1st line therapy, and 7 minutes from hospital arrival to 1st line therapy. The most frequently used antiepileptic drugs were one dose of benzodiazepine (41.7%), phenytoin (76.9%), and additional doses of benzodiazepines (60%) for 1st, 2nd, and 3rd line therapies, respectively. The most frequent etiologies were antiepileptic drug suspension (27.1%), undetermined (25.4%) and acute stroke (11.8%). 62.71% had 0-2 modified Rankin score at discharge. CONCLUSIONS: In this cohort of patients, GCSE was mainly intermittent. Management times differed from the guidelines' recommendations.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".