Patient‐reported outcome measures in pediatric epilepsy: A content analysis using World Health Organization definitions
Bibliographic record
Abstract
OBJECTIVE: Patient-reported outcome (PRO) measures that assess the effect of epilepsy on children's lives include the concepts of health, health-related quality of life (HRQOL), and quality of life (QOL). They also contain varied health and health-related content. Our objectives were to identify what generic and epilepsy-specific PRO instruments are used in childhood epilepsy research and to make explicit their conceptual approach and biopsychosocial content. METHODS: MEDLINE, EMBASE, and PsycINFO were searched from 2001 to 2011 for PRO measures used in pediatric epilepsy. Measures were analyzed on an item-by-item basis according to World Health Organization (WHO) definitions of QOL and the International Classification of Functioning, Disability and Health for Children and Youth (ICF-CY) biopsychosocial health framework to distinguish the conceptual approach within each measure. The health content analysis coded each item according to specific ICF-CY components of body function, activity and participation, environment, or personal factors to determine the health content for each measure. RESULTS: Three generic and 13 epilepsy-specific PRO measures were identified; 10 of 16 measures utilized a biopsychosocial health approach rather than an HRQOL or QOL approach. Content analysis showed that in 11 of 16 measures, >25% of the items represented participation and activity components of the ICF-CY, whereas a high proportion of environment items were found in only one epilepsy-specific measure. SIGNIFICANCE: This comprehensive review provides information aiding clinicians and researchers in the selection of the appropriate PRO instruments for children with epilepsy on the basis of content. Most epilepsy-specific and generic PROs use a biopsychosocial health approach as opposed to a subjective HRQOL/QOL approach to measurement. Clinicians and researchers must be aware of these concepts and content when intending to measure outcomes validly.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".