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 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 | 0.048 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.013 | 0.021 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".