Incidence of First-Episode Status Epilepticus and Risk Factors in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Status epilepticus (SE) is a neurological emergency characterized by prolonged seizures. However, the incidence of first-episode SE is unclear, as estimates vary greatly among studies. Additionally, SE risk factors have been insufficiently explored. Therefore, the objectives of this study were to estimate the incidence of first-episode SE in Ontario, Canada, and estimate the associations between potential sociodemographic and health-related risk factors and first-episode SE. METHODS: We conducted a population-based retrospective cohort study using linked health administrative datasets. We included individuals who completed Canada's 2006 Census long-form questionnaire, lived in Ontario, were between 18 and 105, and had no history of SE. A Cox proportional hazards regression model was used to estimate the hazard ratios for SE within three years associated with each potential risk factor. RESULTS: The final sample included 1,301,700 participants, 140 of whom were hospitalized or had an emergency department visit for first-episode SE during follow-up (3.5 per 100,000 person-years). Older age was the only significant sociodemographic SE risk factor (HR = 1.35, 95% CI = 1.33, 1.37), while health-related risk factors included alcohol or drug abuse (HR = 1.05, 95% CI = 1.02, 1.08), brain tumour or cancer (HR = 1.14, 95% CI = 1.12, 1.15), chronic kidney disease (HR = 1.32, 95% CI = 1.29, 1.36), dementia (HR = 1.42, 95% CI = 1.36, 1.48), diabetes (HR = 1.11, 95% CI = 1.09, 1.12), epilepsy or seizures (HR = 1.05, 95% CI = 1.01, 1.09) and stroke (HR = 1.08, 95% CI = 1.05, 1.11). CONCLUSION: The estimated incidence of SE in a sample of Ontario residents was 3.5 per 100,000 person-years. Older age and several comorbid conditions were associated with higher first-episode SE risk.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".