A Comparison of EQ-5D Index Scores Derived from the US and UK Population-Based Scoring Functions
Bibliographic record
Abstract
The authors recently introduced a new preference-based scoring function for the EQ-5D (D1 model) based on time tradeoff valuations from the general adult US population: In this study, they compared the EQ-5D index scores derived from the US (D1) algorithm to the more familiar UK (N3) algorithm. They compared preference-based EQ-5D index scores for all possible EQ-5D health states and differences in EQ-5D index scores between pairs of EQ-5D health states predicted by the D1 and N3 models. The responsiveness of D1- and N3-predicted EQ-5D index scores was assessed using simulated transitions between EQ-5D health states. The mean (SD) EQ-5D index scores for all 243 health states predicted by the D1 and N3 models were 0.37 (0.23) and 0.14 (0.31), respectively. The mean (SD) absolute difference in EQ-5D index scores for all 29,403 pairs of health states was 0.25 (0.19) and 0.35 (0.27), according to the D1 and N3 models, respectively. The D1 and N3 models were consistent in predicting gains/losses for 27,592 (94%) transitions between EQ-5D health state pairs; Cohen effect size, calculated using these 27,592 consistent transitions, was 1.58 and 1.59 for the D1 and N3 models, respectively. Based on these simulation results, it appears that the D1 model would lead to smaller gains in quality-adjusted life years than the N3 model; however, their responsiveness appears to be similar. Empirical studies are needed to examine whether these 2 EQ-5D scoring functions would lead to different conclusions in cost-utility analyses.
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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 | 0.023 | 0.097 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".