Unprovoked Status Epilepticus: The Prognosis for Otherwise Normal Children With Focal Epilepsy
Bibliographic record
Abstract
OBJECTIVE: To document the effect of unprovoked status epilepticus (SE) on the prognosis for otherwise normal children with focal epilepsy. METHODS: From the Nova Scotia Childhood Epilepsy Study (population-based), we identified patients with focal epilepsy, normal intelligence, and neurologic examination and follow-up ≥ 10 years. We compared those with and without unprovoked SE. RESULTS: One hundred eighty-eight cases had a mean follow-up of 27 ± 5 years with no deaths from SE. Thirty-nine (20%) had SE, 19 of whom experienced their first seizure. The number of episodes of SE was 1 in 27 patients (69%) and 2 to 10 in 12 patients. At onset 9 of 39 (23%) SE patients and 35 of 149 (23%) no-SE patients had specific learning disorders. At follow-up, 11 (28%) SE and 49 (33%) no-SE patients had learning disorders (P = not statistically different [ns]). Grades repeated, high school graduation, and advanced education did not differ. The number of antiepileptic drug (AED) used throughout the clinical course was the same: 22/39 (56%). SE patients used ≤ 2 AEDs versus 99 of 149 (64%) no-SE patients (P = .2). The distribution of patients using 3 to 11 AEDs was similar. The remission rate (seizure-free without AEDs at the end of follow-up) for SE patients was 24 of 39 (61%) versus 99 of 149 (66%) in no-SE (P = .5). Intractable epilepsy occurred in 15% SE and 11% of no-SE cases. CONCLUSIONS: SE often recurs but apparently has little influence on long-term intellectual and seizure outcome in normally intelligent children with focal epilepsy.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".