Posterior circulation lesions are more frequently associated with early seizures after a stroke
Bibliographic record
Abstract
Background. Early seizures (ES) following stroke are prevalent among the elderly population, representing the most common type of acquired seizures. This study aimed to determine the incidence of ES and investigate potential associations with various clinical and radiological factors. Materials and Methods. 260 stroke patients (mean age 72±13.2, 48.5% females) were prospectively enrolled and followed. Baseline demographic data, clinical data, stroke subtype, ES occurrence, National Institutes of Health Stroke Scale (NIHSS) scores, and Alberta Stroke Program Early CT Score (ASPECT) were collected and analyzed. Results. ES was observed in 11.6% of patients with ischemic stroke compared to 7.1% among patients with hemorrhagic stroke. ES occurred more frequently in those with posterior circulation stroke (18.5% vs. 7.1%, P=0.008) and those with NIHSS >15 (19.4% vs. 8.4%, P=0.04). In a logistic regression analysis that adjusted for vascular risk factors and NIHSS, posterior circulation stroke remained significantly associated with ES, with an odds ratio of 3.14 (95% CI 1.20 to 7.73, P=0.012). Conclusions. This study revealed that ES following stroke is more common in patients with posterior circulation lesions. These findings emphasize the need for further investigation into additional factors that may influence ES occurrence and its impact on stroke management and patient outcomes.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".