ADHD, Neurological Correlates and Health‐related Quality of Life in Severe Pediatric Epilepsy
Bibliographic record
Abstract
PURPOSE: ADHD is reported as a frequent comorbidity in pediatric epilepsy. We aimed to clarify the prevalence of ADHD, its neurological correlates and the role of ADHD in health-related quality of life (HRQOL) in children with severe epilepsy. METHOD: Data from the ADHD Rating Scale-IV (ADHD-RS-IV) from 203 children (mean age = 11.8, SD=3.8) from a tertiary center serving children with severe epilepsy were reviewed. RESULTS: Inattention was frequently elevated in the sample (40% vs. 18% for hyperactivity-impulsivity). Age of onset, epilepsy duration, and seizure frequency were not related to severity of inattention or hyperactivity-impulsivity. Over 60% of children met screening criteria for ADHD-Inattentive subtype (ADHD-I) or ADHD-Combined Inattentive/Hyperactive-Impulsive subtype (ADHD-C). Compared to ADHD-I, ADHD-C was associated with earlier onset of seizures, generalized epilepsy, lower adaptive level, and in normally developing children, a higher degree of intractability compared to ADHD-I. ADHD-I was more prevalent in localization-related epilepsy, and there was a trend for a higher use of AEDs with cognitive side effects in this group. ADHD was associated with poor HRQOL: children with ADHD-I and ADHD-C had a two- and four-fold likelihood of low HRQOL, respectively, compared to non-ADHD children. CONCLUSIONS: Children seen at tertiary care centers for severe epilepsy are at high risk for attention problems and ADHD, and ADHD is a significant predictor of poor HRQOL in epilepsy, particularly in the case of ADHD-C. ADHD occurring in the context of severe epilepsy appears to be associated with specific neurological characteristics, which has implications for comorbidity models of ADHD and 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 | 0.002 |
| 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) | 0.001 | 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".