Small differences in EQ-5D-5L health utility scores were interpreted differently between and within respondents
Bibliographic record
Abstract
OBJECTIVES: This study aims to determine how population-based health-utility score (HUS) differences reflect individuals' health preferences using responses from the Canadian EQ-5D-5L Valuation Study, including time trade-off (TTO) and discrete-choice experiment (DCE) tasks (n = 1073). STUDY DESIGN AND SETTING: Cardinal TTO responses were transformed into pairwise comparisons to yield ordinal TTO responses. We investigated how EQ-5D-5L HUS differences differ from participants' stated cardinal preferences, and determined the smallest HUS difference that may be expected to represent participants' ordinal preferences. RESULTS: HUS differences near zero have 30.6% (95% confidence interval: 29.1-31.9%) probability of representing a tie in individuals' TTO values. Differences in EQ-5D-5L HUS of -0.054 (-0.071 to -0.029) and 0.047 (0.026-0.076) maximized the sensitivity and specificity of discriminating transitions to worse/better health states. For small HUS differences of ±0.03 to ±0.07, the magnitude of respondents' average TTO difference on the cardinal scale was 0.17 and 0.35 whether ties were included or excluded, respectively. Absolute HUS differences between 0.042 and 0.062 had a 50% probability of representing respondents' ordinal preferences. CONCLUSION: A HUS needs to be large enough to reflect individuals' stated health preferences, which may lend support to the application of a minimally important difference for decision-making.
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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 | 0.024 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".