Epilepsy‐specific patient‐reported outcome measures of children's health‐related quality of life: A systematic review of measurement properties
Bibliographic record
Abstract
OBJECTIVE: To identify and appraise published evidence of the measurement properties for epilepsy-specific patient-reported outcome measures (PROMs) of children's health-related quality of life (HRQoL). METHODS: We searched multiple databases for studies evaluating the measurement properties of English-language epilepsy-specific PROMs of children's HRQoL. We assessed the methodological quality using the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) guidance. We extracted data about the content validity, construct validity, internal consistency, test-retest reliability, proxy reliability, responsiveness, and precision, and assessed the measurement properties with reference to standardized criteria. RESULTS: We identified 27 papers that evaluated 11 PROMs. Methodological quality was variable. Construct validity, test-retest reliability, and internal consistency were more commonly assessed. Quality of Life in Childhood Epilepsy (QoLCE) questionnaires are parent-reported and evaluated more than other PROMs; QoLCE-55 has good and replicated evidence for structural and construct validity and internal consistency. Health-Related Quality of Life Measure for Children with Epilepsy (CHEQoL) has both child and parent-reported versions and good evidence of content, structural, and construct validity. SIGNIFICANCE: This review identified two leading candidate epilepsy-specific PROMs for measuring health-related quality of life in children. Establishing evidence of the responsiveness of PROMs is a priority to help the interpretation of meaningful change scores.
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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 | 0.028 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".