Executive Dysfunction Is a Significant Predictor of Poor Quality of Life in Children with Epilepsy
Bibliographic record
Abstract
PURPOSE: Based on prior research indicating poor health-related quality of life (HRQOL) in children with attention-deficit/hyperactivity disorder, we investigated (1) whether executive functioning deficits were related to poor HRQOL in children with epilepsy, (2) how important these variables were in comparison to known predictors of HRQOL such as neurological factors, and (3) the extent to which clinical-level impairments in executive dysfunction predispose children to low HRQOL. METHOD: Data included scores on the Behavior Rating Inventory of Executive Function (BRIEF) and HRQOL scales (The Impact of Childhood Illness Scale [ICI] and Hague Restrictions in Epilepsy Scale [HARCES]) for 121 children (mean age = 11.9, SD = 3.6) from a tertiary center serving children with severe epilepsy. RESULTS: Correlations between the BRIEF and ICI total and subscore domains (child, parent, family, and treatment) were generally significant and moderate (e.g., r > or = 0.30, p < or = 0.001). BRIEF Global Executive Composite, number of antiepileptic drugs (AEDs), number of prior AEDs, and adaptive level all emerged as significant and unique predictors of HRQOL (R(2)= 0.36, adj. R(2)= 0.33, p < 0.0001). A clinically elevated BRIEF was associated with a twofold risk of low HRQOL (odds ratio = 2.21, p = 0.03). CONCLUSIONS: Executive dysfunction appears to exert a broad adverse influence on HRQOL in children with epilepsy, with clinical-level impairments in executive dysfunction contributing to a twofold increase in the likelihood of poor HRQOL. The constellation of executive dysfunction, low adaptive level, high medication load, and a history of several failed AEDs are risk factors for poor HRQOL in children with 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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".