Impact of seizures on morbidity and mortality after stroke: a Canadian multi‐centre cohort study
Bibliographic record
Abstract
INTRODUCTION: Limited information is available about the impact of seizures on stroke outcome, health care delivery and resource utilization. OBJECTIVE: To determine whether the presence of seizures after stroke increases disability, mortality and health care utilization (length of hospital stay, ICU admission, consults, discharge to a long-term care facility). METHODS: This cohort study included consecutive patients with acute stroke between July 2003 and June 2005 from the Registry of the Canadian Stroke Network (RCSN), the largest clinical database of patients in Canada with acute stroke seen at selected acute care hospitals. We compared clinical characteristics and outcomes amongst patients experiencing stroke without and with seizures occurring during inpatient stay. Main outcome measures included: case-fatality, disability at discharge, length-of-stay, and discharge disposition. A logistic regression analysis was used to determine whether the presence of seizures was associated with poor stroke outcomes. RESULTS: Amongst 5027 patients included in the study; seizures occurred in 138 (2.7%) patients with stroke. Patients with seizures had a higher mortality at 30-day (36.2% vs. 16.8%, P < 0.0001) and at 1-year post-stroke (48.6% vs. 27.7%, P < 0.001), longer hospitalization, and greater disability at discharge (P < 0.001). Multivariate analysis revealed that stroke severity, hemorrhagic stroke, and presence of neglect were associated to occurrence of seizures after stroke. CONCLUSIONS: The presence of seizures after stroke was associated with increased resources utilization, length of hospital stay, whilst decreasing both 30-day and 1-year survival. Quality improvement strategies targeting patients with seizures may help optimize the management of this subgroup of more disabled patients.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".