Epilepsy and proxy‐reported health‐related quality of life in children and young people with non‐ambulatory cerebral palsy
Bibliographic record
Abstract
AIM: To assess the association between epilepsy characteristics and proxy-reported health-related quality of life (HRQoL) in children and young people with non-ambulatory cerebral palsy (CP) and seizures. METHOD: This was a cross-sectional study of 164 children and young people (74 females, 90 males; mean age 10 years 6 months, range 2-21 years, SD 5 years 5 months). Caregivers completed the Child Health Index of Life with Disabilities (CPCHILD) in an outpatient setting. We utilized univariable linear regression and multivariable modeling to study relationships between variables and CPCHILD scores. RESULTS: Gross Motor Function Classification System levels were 37% IV and 63% V. Sociodemographic factors included the Child Opportunity Index (median 51, interquartile range [IQR] 25-80). A median of 2 (IQR 1-3) antiseizure medications (ASMs) were used, and days with seizures ranged from 0 (30%) to 28 (20%) days in the previous 4 weeks. Total CPCHILD scores decreased 2.3 points for each ASM (95% confidence interval [CI] -4.1 to -0.42). Compared to persons with focal epilepsy, those with generalized epilepsy had lower total CPCHILD scores (-5.7; 95% CI -11 to -0.55). Number of days with seizures was not associated with total CPCHILD scores. INTERPRETATION: Proxy-reported HRQoL was affected by epilepsy-specific features in children and young people with severe CP. WHAT THIS PAPER ADDS: Health-related quality of life (HRQoL) was lower with increasing numbers of antiseizure medications. Overall quality of life (QoL) scores were lower by a similar amount, independent of seizure frequency. HRQoL was lower in persons with recent hospital admissions for 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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".