P.102 Childhood Absence Epilepsy: Prevalence of treatment resistance and neuropsychiatric comorbidity.
Bibliographic record
Abstract
Background: Seizures in childhood absence epilepsy (CAE) are usually easily controlled with anti-seizure medications (ASMs). Factors predictive of treatment resistance remain unclear. Our objectives were to assess prevalence of neuropsychiatric problems and factors influencing refractoriness in a cohort of CAE at a single centre. Methods: We retrospectively reviewed patients with CAE (ILAE 2017 classification) diagnosed between January 1999 and December 2016 with at least 1-year follow-up. Treatment resistance was defined as failure to respond to two or more appropriate ASMs. Exclusion criteria included eyelid myoclonia with absence, myoclonic absence, and generalized tonic-clonic (GTC) seizure before developing absences. Results: The study population comprised 164 patients (65 males) 6.25-year-old on average at absence onset. 22% had treatment-resistant seizures. The first ASM was Ethosuximide in 63.4%, Valproic acid in 23.2%, and Lamotrigine in 6.7%. Statistical differences between response groups included developing a second seizure type specifically GTC, the second and third ASM, and absence of EEG normalization. At last follow-up, 43.3% of children were seizure-free off ASMs. 32.9% of children had learning disabilities, 28% ADHD, and 12.8 % anxiety. Conclusions: 22% of children with CAE had treatment-resistant seizures. Photoparoxysmal response was not predictive of treatment resistance. Neuropsychiatric problems were common with learning disabilities increased with refractory absences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".