Incidence and management of seizures after ischemic stroke
Bibliographic record
Abstract
Objective: We conducted a meta-analysis of the incidence of early and late seizures following ischemic stroke as well as a systematic review of their pharmacologic treatment. Methods: Observational studies that reported incidence of seizures following ischemic stroke and those that reported treatment response to any particular antiepileptic drugs (AEDs) were included. Risk of bias was assessed by predefined study characteristics. Random effects meta-analysis was conducted for all studies where data were available for the incidence of early and late stroke-related seizures. Heterogeneity was measured with I2 statistic and sensitivity analyses were performed using prespecified variables. A qualitative synthesis of studies reporting use of AEDs for stroke-related seizures was performed. Results: Forty-one studies from 10,554 articles were identified; 35 studies reported incidence of stroke-related seizures and 6 studies reported effects of specific AEDs. Most studies were of low to moderate quality. Rate of early seizures was 3.3% (95% confidence interval 2.8%–3.9%, I2 = 92.8%), while the incidence of late seizures or epilepsy was 18 per 1,000 person-years (95% confidence interval 1.5–2.2, I2 = 94.1%). The high degree of heterogeneity could not be explained from the sensitivity analyses. For management of stroke-related seizures, no single AED was found to be more effective over others, though newer AEDs were associated with fewer side effects. Conclusions: The burden of stroke-related seizures and epilepsy due to ischemic stroke is substantial. Further studies are required to determine risk factors for epilepsy following ischemic stroke and optimal secondary prevention.
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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.025 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".