Testing the Valuation of the EQ-5D-Y-5L in Adults and Adolescents: Results From a 5-Country Study and Implications for the Descriptive System
Bibliographic record
Abstract
OBJECTIVES: The EQ-5D-Y-5L (Y-5L) is a new health-related quality-of-life instrument for children and adolescents. Value sets for the Y-5L are planned. This article aimed to test the ability of adult and adolescent respondents to differentiate the ordinal levels of the Y-5L in valuation tasks and to explore the characteristics of stated preferences for the Y-5L between adults and adolescents. METHODS: We collected latent-scale discrete choice experiment data via an online survey of adults (≥18 years) and adolescents (12-17 years) in Australia, Canada, China, The Netherlands, and Spain. A D-Efficient design consisting of 192 choice pairs was grouped into 16 blocks of 12 choice tasks per respondent. We used mixed-logit models to analyze the data and incremental dummies to represent movements from a less-severe level to its consecutive more-severe level. RESULTS: We did not observe preference inversions in adults or adolescents (ie, no statistically significant positive coefficients on the incremental dummies). Adults showed similar preferences for the Y-5L in terms of dimension importance: Pain/Discomfort was considered the most important dimension in all countries except for China; Looking After Myself and Usual Activity were the least important dimensions. In contrast, Mobility was considered the most important dimensions by adolescents in Canada, Spain, and China. CONCLUSIONS: Adults could differentiate between the Y-5L level labels in valuation tasks, whereas more randomness was observed in adolescents' choices. Observed differences between adult and adolescent stated preferences for the Y-5L raise questions about how these preferences should be reflected in cost-effectiveness analysis.
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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 | 0.026 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".