Epilepsy Surgery in Adult Stroke Survivors with New-Onset Drug-Resistant Epilepsy
Bibliographic record
Abstract
BACKGROUND: Despite its effectiveness, surgery for drug-resistant epilepsy is underutilized. However, whether epilepsy surgery is also underutilized among patients with stroke-related drug-resistant epilepsy is unclear. Therefore, our objectives were to estimate the rates of epilepsy surgery assessment and receipt among patients with stroke-related drug-resistant epilepsy and to identify factors associated with these outcomes. METHODS: We used linked health administrative databases to conduct a population-based retrospective cohort study of adult Ontario, Canada residents discharged from an Ontario acute care institution following the treatment of a stroke between January 1, 1997, and December 31, 2020, without prior evidence of seizures. We excluded patients who did not subsequently develop drug-resistant epilepsy and those with other epilepsy risk factors. We estimated the rates of epilepsy surgery assessment and receipt by March 31, 2021. We planned to use Fine-Gray subdistribution hazard models to identify covariates independently associated with our outcomes, controlling for the competing risk of death. RESULTS: We identified 265,081 patients who survived until discharge following inpatient stroke treatment, 1,902 (0.7%) of whom subsequently developed drug-resistant epilepsy (805 women; mean age: 67.0 ± 13.1 years). Fewer than six (≤0.3%) of these patients were assessed for or received epilepsy surgery before the end of follow-up (≤55.5 per 100,000 person-years). Given that few outcomes were identified, we could not proceed with the multivariable analyses. CONCLUSIONS: Patients with stroke-related drug-resistant epilepsy are infrequently considered for epilepsy surgery that could reduce morbidity and mortality.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".