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Record W4406351943 · doi:10.1212/wnl.0000000000210231

Antiseizure Medications in Poststroke Seizures

2025· review· en· W4406351943 on OpenAlexaff
Shubham Misra, Selena Wang, Terence J. Quinn, Jesse Dawson, Johan Zelano, Tomotaka Tanaka, James C. Grotta, Erum Khan, Nitya Beriwal, Melissa Funaro, Sravan Perla, Priya Dev, David Larsson, Taimoor Hussain, David S. Liebeskind, Clarissa Lin Yasuda, Hamada Altalib, Hitten P. Zaveri, Amr Elshahat, Gazala Hitawala, Abhishek Pathak, Fabien Scalzo, Masafumi Ihara, Katharina S. Sunnerhagen, Matthew R. Walters, Yize Zhao, Nathalie Jetté, Scott E. Kasner, Patrick Kwan, Nishant K. Mishra

Bibliographic record

VenueNeurology · 2025
Typereview
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsUniversity of Calgary
FundersNational Heart, Lung, and Blood Institute
KeywordsDiscontinuationMedicineLevetiracetamPhenytoinPiracetamRandomized controlled trialCarbamazepineAdverse effectOdds ratioInternal medicineMeta-analysisEpilepsyPediatricsAnesthesiaPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: The most effective antiseizure medications (ASMs) for poststroke seizures (PSSs) remain unclear. We aimed to determine outcomes associated with ASMs in people with PSS. METHODS: We systematically searched electronic databases for studies on patients with PSS on ASMs. Our outcomes were seizure recurrence, adverse events, drug discontinuation rate, and mortality. We assessed the risk of bias using Cochrane Risk of Bias tool for randomized controlled trials and Risk Of Bias In Non-randomized Studies of Interventions tools. Using levetiracetam as the reference treatment, we conducted a frequentist network meta-analysis and determined the certainty of evidence using the Grading of Recommendations Assessment, Development, and Evaluation methodology. RESULTS: Our search yielded 15 studies (3 randomized, 12 nonrandomized, N = 18,676 patients (121 early and 18,547 late seizures), 60% male, mean age 69 years) comparing 13 ASMs. Three studies had moderate and 12 had high risk of bias. Seizure recurrence was 24.8%. Compared with levetiracetam, very low-certainty evidence suggested that phenytoin was associated with higher seizure recurrences (odds ratio [OR] 7.3, 95% CI 3.7-14.5) and more adverse events (OR 5.2, 95% CI 1.2-22.9). Low-certainty evidence suggested that carbamazepine (OR 1.8, 95% CI 1.5-2.2) and phenytoin (OR 1.9, 95% CI 1.4-2.8) were associated with high drug discontinuation rates. Moderate to high-certainty evidence suggested that valproic acid (OR 4.7, 95% CI 3.6-6.3) and phenytoin (OR 8.3, 95% CI 5.7-11.9) were associated with higher mortality rates. Considering all treatments and using the GRADE approach for treatment ranking, very low-certainty evidence suggested that eslicarbazepine, lacosamide, and levetiracetam had the fewest seizure recurrences. Low to very low-certainty evidence suggested that lamotrigine had the fewest adverse events and drug discontinuations, whereas lamotrigine and levetiracetam exhibited low mortality rates with moderate-certainty evidence. DISCUSSION: We found that levetiracetam and lamotrigine may be safe and tolerable ASMs for PSS. Despite ASM use, the seizure recurrence rate remains high in the PSS population. Owing to bias and confounding risks, these findings should be interpreted cautiously. TRIAL REGISTRATION INFORMATION: PROSPERO: CRD42022363844.

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 imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.053
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.053
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0060.003
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.035
GPT teacher head0.404
Teacher spread0.369 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations10
Published2025
Admission routes1
Has abstractyes

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