Post-stroke seizures in animal models: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Post-stroke seizures (PSS) are a common complication of stroke and can exacerbate neurological injury, yet their study in preclinical models remains limited. Understanding the relationship between PSS and outcomes in animal models is critical for improving translational research and informing therapeutic strategies. Objective To systematically review and meta-analyze the incidence, consequences, and methodological quality of studies investigating PSS in animal models of ischemic stroke (IS) and intracerebral hemorrhage (ICH). Methods A systematic search of Embase, Medline, Scopus, and Web of Science (June 2024, updated May 2025) identified original, peer-reviewed animal studies published after 1999 that reported seizures and outcomes (lesion volume, neurological deficit scores, behavior, edema, inflammation) without interventional treatments. Data extraction, risk-of-bias assessment, and a random-effects meta-analysis was performed for lesion volume. Results Of 6,005 studies screened, 10 met inclusion criteria, with eight eligible for meta-analysis. Seizure incidence ranged from 17.5–82% in focal ischemia and 45–67% in ICH models. Lesion volume was the most commonly measured outcome. Meta-analysis revealed that seizures were associated with larger lesion volumes in focal ischemia models (Hedge’s G = 1.598, p = 0.038) but not in ICH models (Hedge’s G = 0.180, p = 0.468). Across studies, seizures were linked to more severe neurological deficits in focal ischemia but showed no consistent effect in ICH. Risk-of-bias assessment indicated high risk in all studies, with frequent methodological limitations including lack of random outcome assessment, use of only young male animals, and absence of a priori sample size calculations. Publication bias was suggested by funnel plot asymmetry. Conclusion This review highlights a scarcity of rigorous preclinical studies on PSS, substantial heterogeneity across animal models, and methodological limitations that hinder translatability. Findings suggest a differential impact of stroke type on seizure outcomes, with focal ischemia-associated seizures linked to larger lesions and poorer neurological function. Future research should employ long-term, rigorously designed studies using diverse animal populations, standardized seizure monitoring, and careful reporting to enhance clinical relevance and guide therapeutic development.
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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 | 0.027 | 0.060 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.048 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".