Comparison of lacosamide, levetiracetam, and valproate as second‐line therapy in adult status epilepticus: Analysis of a large cohort
Bibliographic record
Abstract
We compared the efficacy of lacosamide to other frequently used second-line anti-seizure medications (ASMs) for adult status epilepticus (SE) by conducting a retrospective analysis of an institutional SE registry between January 2013 and December 2022. Clinical outcomes assessed at discharge were categorized as return to baseline, new disability, or death; we also considered SE termination after the second-line ASM and the need for mechanical ventilation. Potential confounders included the Status Epilepticus Severity Score (STESS), sex, adequacy of initial SE treatment, treatment delay, and potentially fatal etiology. Over 10 years, 961 adult SE episodes were analyzed; 868 were treated with the following second-line ASMs: 413 levetiracetam (47.6%), 110 valproate (12.7%), and 75 lacosamide (8.6%), as well as lower rates of 18 other ASMs including benzodiazepines (not further analyzed). Univariable analysis identified STESS, treatment delay, and adequacy of initial SE treatment as potential confounders. On multivariable analysis adjusting for these variables, patients with episodes treated with second-line lacosamide, levetiracetam, or valproate demonstrated statistically equivalent rates of seizure cessation, need for mechanical ventilation, and clinical outcomes at hospital discharge. We conclude that lacosamide appears to represent a reasonable alternative to levetiracetam and valproate, and warrants consideration for inclusion in future randomized controlled trials for control of SE.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".