P.028 Incidence and management of seizures and epilepsy after ischemic stroke: a systematic review
Bibliographic record
Abstract
Background: Seizures and epilepsy are well-recognized complications after stroke. However, the reported incidence varies and so does their management. Methods: We conducted a systematic review and sought observational studies that reported incidence of seizures and/or epilepsy following arterial ischemic stroke in adults, and those that reported the management of epilepsy, specifically the use of EEG to determine the diagnosis, timing of initiation of anti-epileptic drug (AED), and the treatment response to AEDs. We systematically searched in Medline including Pre-Medline and EMBASE databases from their inception to October 1, 2015. First the titles and then the articles were reviewed and rated by two independent reviewers, and disagreements were resolved by consultation with a third reviewer. A pre-set data abstraction form was used for extracting the information of interest. Results: A total of 11,815 titles were found from the initial search strategy across all databases following de-duplication. Of these 130 studies are included for full text review. The adjudication process is underway and the reviewers are sifting through these studies to select the studies that will be included in the final review. Conclusions: Understanding incidence and management of post-stroke epilepsy is important to improve the quality of life of stroke survivors.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".