C.3 Childhood Absence Epilepsy: Prevalence of neuropsychiatric comorbidities and predictors of treatment resistance
Bibliographic record
Abstract
Background: Seizures in childhood absence epilepsy (CAE) are typically 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 treatment resistance in a cohort of CAE at a single centre. Methods: We retrospectively reviewed patients with CAE diagnosed between January 1999 and December 2016 with at least 1-year follow-up. Treatment resistance was defined as failure to respond to two appropriate ASMs. Exclusion criteria included eyelid myoclonia with absence, myoclonic absence, and generalized tonic-clonic (GTC) seizures prior to absence seizures. Results: The study population comprised 164 patients (65 males) with mean age at seizures onset of 6.1years. 21% had treatment-resistant seizures. The first ASM was Ethosuximide (63.4%), Valproic acid (23.2%), and Lamotrigine (6.7%). 32.9% of children had learning disabilities, 28.7% ADHD, and 12.8 % anxiety. A stepwise binary logistic regression analysis identified GTC seizures, learning disability (LD) and ongoing general spike and wave on EEG as predictors of treatment resistance. At last follow-up (average of 5.4 years), 43.3% of children were seizure-free off ASMs. Conclusions: 21% of children with CAE had treatment-resistant seizures. LD, GTC and failure of normalization of the EEG were associated with treatment resistance.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".