Development and assessment of the Quality of Life in Childhood Epilepsy Questionnaire (QOLCE‐16)
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to develop and validate a brief version of the Quality of Life in Childhood Epilepsy Questionnaire (QOLCE). A secondary aim was to compare the results described in previously published studies using the QOLCE-55 with those obtained using the new brief version. METHODS: Data come from 373 children involved in the Health-related Quality of Life in Children with Epilepsy Study, a multicenter prospective cohort study. Item response theory (IRT) methods were used to assess dimensionality and item properties and to guide the selection of items. Replication of results using the brief measure was conducted with multiple regression, multinomial regression, and latent mixture modeling techniques. RESULTS: IRT methods identified a bi-factor graded response model that best fits the data. Thirty-nine items were removed, resulting in a 16-item QOLCE (QOLCE-16) with an equal number of items in all 4 domains of functioning (Cognitive, Emotional, Social, and Physical). Model fit was excellent: Comparative Fit Index = 0.99; Tucker-Lewis Index = 0.99; root mean square error of approximation = 0.052 (90% confidence interval [CI] 0.041-0.064); weighted root mean square = 0.76. Results that were reported previously using the QOLCE-55 and QOLCE-76 were comparable to those generated using the QOLCE-16. SIGNIFICANCE: The QOLCE-16 is a multidimensional measure of health-related quality of life (HRQoL) with good psychometric properties and a short-estimated completion time. It is notable that the items were calibrated using multidimensional IRT methods to create a measure that conforms to conventional definitions of HRQoL. The QOLCE-16 is an appropriate measure for both clinicians and researchers wanting to record HRQoL information in children with epilepsy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".