Parallel Valuation: A Direct Comparison of EQ-5D-3L and EQ-5D-5L Societal Value Sets
Bibliographic record
Abstract
OBJECTIVE: To compare and contrast EQ-5D-5L (5L) and EQ-5D-3L (3L) health state values derived from a common sample. METHODS: Data from the 2017 US EQ-5D valuation study were analyzed. Value sets were estimated with random-effects linear regression based on composite time trade-off (cTTO) valuations for 3L and 5L health states with 2 approaches to model specification: main effects only and additional N3/N45 terms. Properties of the descriptive system and value set characteristics were compared by examining distributions of predicted index scores, ceiling effects, and single-level transition values from adjacent corner health states. Mean transition values were calculated for all predicted 3L and 5L health states and plotted against baseline index scores. RESULTS: A total of 1062 respondents were included in the analysis. The observed mean cTTO values for the worst possible 3L and 5L health states were -0.423 and -0.343, respectively. The range of scale was larger with the 3L, compared to the 5L, for both main effects and N term models. Values for the mildest 5L health states (range, 0.857-0.924) were similar to 11111 for the 3L. Parameter estimates for matched dimension levels differed by <|0.07| except for the most severe level of Mobility. For the main effects model, 3L mean transition values were greater for more severe baseline 3L index scores, whereas 5L mean transition values remained constant irrespective of the baseline index score. CONCLUSIONS: Compared to the 3L, the 5L exhibited a lower ceiling effect and improved measurement properties. There was a larger range of scale for the 3L compared to 5L; however, this difference was driven by differences in preference for the most severe level of problems in Mobility.
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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 | 0.010 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".