Memory and Health-Related Quality of Life in Severe Pediatric Epilepsy
Bibliographic record
Abstract
OBJECTIVES: The purpose of this paper was to investigate the contributions of sociodemographic, neurologic, and neuropsychological variables to health-related quality of life (HRQoL) in children with epilepsy and high seizure burden. Focus was placed on the relationship between memory and HRQoL, which has not been previously investigated. METHODS: Ninety children with epilepsy receiving clinical care at a tertiary-level children's hospital were retrospectively identified. Primary assessment measures were verbal memory (California Verbal Learning Test-Children's Version) and HRQoL. Other neuropsychological variables included intellectual function, executive function, emotional and behavioral function, and adaptive function. Sociodemographic and neurologic variables were extracted from chart review. RESULTS: No significant correlations were found between HRQoL and sociodemographic or neurologic variables. Moderate correlations were found between neuropsychological variables and HRQoL. Emotional function (Child Behavior Checklist) and verbal memory (California Verbal Learning Test-Children's Version) emerged as significant predictor variables of HRQoL. Low verbal memory was associated with a twofold risk of low HRQoL, emotional and behavioral difficulty with a 10-fold risk, and the combination of emotional and behavioral difficulty and low verbal memory with a 17-fold risk. CONCLUSIONS: Verbal memory and emotional and behavioral difficulty are associated with increased risk of low HRQoL, even when other important variables are considered in children with high seizure burden. The results reinforce the importance of neuropsychological assessment in clinical care in pediatric epilepsy and suggest important areas of focus for psychological intervention.
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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 | 0.000 |
| 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".