Development and assessment of a shortened <scp>Q</scp>uality of <scp>L</scp>ife in <scp>C</scp>hildhood <scp>E</scp>pilepsy <scp>Q</scp>uestionnaire (<scp>QOLCE</scp>‐55)
Bibliographic record
Abstract
OBJECTIVE: To develop and validate a shortened version of the Quality of Life in Childhood Epilepsy Questionnaire (QOLCE). A secondary aim was to compare baseline risk factors predicting health-related quality of life (HRQoL) in children newly diagnosed with epilepsy, as identified using the original and shortened version. METHODS: Data came from the Health-Related Quality of Life in Children with Epilepsy Study (HERQULES, N = 373), a multicenter prospective cohort study. Principal component analysis reduced the number of items from the original QOLCE, and factor analysis was used to assess the factor structure of the shortened version. Convergent and divergent validity was assessed by correlating the Child Health Questionnaire (CHQ) with the shortened QOLCE. Multiple regression identified risk factors at diagnosis for HRQoL at 24 months. RESULTS: A four-factor, higher-order, 55-item solution was obtained. A total of 21 items were removed. The final model represents functioning in four dimensions of HRQoL: Cognitive, Emotional, Social, and Physical. The shortened QOLCE demonstrated acceptable fit: Bentler's Comparative Fit Index = 0.944; Tucker-Lewis Index = 0.942; root mean square approximation = 0.058 (90% CI: 0.056-0.061); weighted root mean square residuals (WRMR) = 1.582, and excellent internal consistency (α = 0.96, subscales α > 0.80). Factor loadings were good (first-order: λ = 0.66-0.93; higher-order λ = 0.66-0.85; p < 0.001 for all). The shortened QOLCE scores correlated strongly with similar subscales of the Child Health Questionnaire (ρ = 0.38-0.70) while correlating weakly with dissimilar subscales (ρ = 0.30-0.31). While controlling for HRQoL at diagnosis, predictors for better HRQoL at 24 months were the following: no cognitive problems reported (p = 0.001), better family functioning (p = 0.014), fewer family demands (p = 0.008), with an interaction between baseline HRQoL and cognitive problems (p = 0.011). SIGNIFICANCE: Results offer initial evidence regarding reliability and validity of the proposed 55-item shortened version of the QOLCE (QOLCE-55). The QOLCE-55 produced results on risk factors consistent with those found using the original measure. Given the fewer items, QOLCE-55 may be a viable option reducing respondent burden when assessing 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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".