Incidence and risk factors of post-stroke seizures and epilepsy: systematic review and meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Due to variability in reports, the aim of this meta-analysis was to evaluate the incidence and risk factors of post-stroke early seizures (ES) and post-stroke epilepsy (PSE). METHODS: The MEDLINE, EMBASE and Web of Science databases were searched for post-stroke ES/PSE articles published on any date up to November 2020. Post-stroke ES included seizures occurring within 7 days of stroke, and PSE included at least one unprovoked seizure. Using random effects models, the incidence and risk factors of post-stroke ES and PSE were evaluated. The study was retrospectively registered with INPLASY (INPLASY2023100008). RESULTS: Of 128 included studies in total, the incidence of post-stroke ES was 0.07 (95% confidence interval [CI] 0.05, 0.10) and PSE was 0.10 (95% CI 0.08, 0.13). The rates were higher in children than adults. Risk factors for post-stroke ES included hemorrhagic stroke (odds ratio [OR] 2.14, 95% CI 1.44, 3.18), severe strokes (OR 2.68, 95% CI 1.73, 4.14), cortical involvement (OR 3.09, 95% CI 2.11, 4.51) and hemorrhagic transformation (OR 2.70, 95% CI 1.58, 4.60). Risk factors for PSE included severe strokes (OR 4.92, 95% CI 3.43, 7.06), cortical involvement (OR 3.20, 95% CI 2.13, 4.81), anterior circulation infarcts (OR 3.28, 95% CI 1.34, 8.03), hemorrhagic transformation (OR 2.81, 95% CI 1.25, 6.30) and post-stroke ES (OR 7.24, 95% CI 3.73, 14.06). CONCLUSION: Understanding the risk factors of post-stroke ES/PSE may identify high-risk individuals who might benefit from prophylactic treatment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it