Critical Care Management of Status Epilepticus at a Tertiary Care University Hospital
Bibliographic record
Abstract
BACKGROUND: Status epilepticus (SE) is a neurological emergency associated with significant morbidity and mortality. The objective of this study was to review the critical care management of patients with SE focusing on antiepileptic drugs (AEDs) as well as to determine the optimal dosing strategies of phenytoin (PHT) and predictors of its effectiveness. METHODS: A retrospective chart review of adult patients with SE admitted to the University of Alberta Hospital, Canada, was conducted. RESULTS: Fifty-six admissions were included. Benzodiazepines (BDZs) were initially given in 89% of our patients. Following BDZs, PHT and levetiracetam were the most commonly initiated AEDs as first- and second-line agents and were deemed effective in 30/44 and 5/11 patients, respectively. Patients who received a PHT loading dose (LD) of 1000 mg were less likely to reach target levels compared with a weight-based LD ≥15 mg/kg (29% vs. 60%). Likewise, patients who received a maintenance dose (MD) of 300 mg/day were less likely to reach target compared with 400 mg/day or >5 mg/kg per day; however, this did not reach statistical significance. Three variables were found to be associated with PHT effectiveness: tonic-clonic SE (OR 5.01, 95% CI 1.02-24.7, p = 0.048), history of seizures and BMI <30 kg/m2 (OR 0.16, 95% CI 0.03-1.07, p = 0.059). CONCLUSIONS: Further studies of the predictors of PHT effectiveness, specifically obesity, are necessary to help individualize care. Finally, we suggest that PHT should be loaded according to the guidelines as 20 mg/kg followed by an MD of at least 400 mg/day or >5 mg/kg per day.
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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".