Valuations of EQ-5D Health States
Bibliographic record
Abstract
PURPOSE: We sought to compare directly elicited valuations for EQ-5D health states between the US and UK general adult populations. METHODS: We analyzed data from 2 EQ-5D valuation studies where, using similar time trade-off protocols, values for 42 common health states were elicited from representative samples of the US and UK general adult populations. First, US and UK population mean valuations were estimated and compared for each health state. Second, random-effect models were used to compare the US and UK valuations while adjusting for known predictors of EQ-5D valuations (ie, age, sex, health state descriptors) and to investigate whether and how the valuations differ. RESULTS: Population mean valuations of the 42 health states ranged from -0.38 to 0.88 for the United States and from -0.54 to 0.88 for the United Kingdom, with the US mean scores being numerically higher than the UK for 39 health states (mean difference: 0.11; range: -0.01 to 0.25). After adjusting for the main effects of known predictors, the average difference in valuations was 0.10 (P < 0.001). The magnitude of the difference in the US and UK valuations was not constant across EQ-5D health states; greater differences in valuations were present in health states characterized by extreme problems. CONCLUSIONS: Meaningful differences exist in directly elicited TTO valuations of EQ-5D health states between the US and UK general populations. Therefore, EQ-5D index scores generated using valuations from the US general population should be used for studies aiming to reflect health state preferences of the US general public.
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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 | 0.015 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".