Systematic Review and Empirical Comparison of Contemporaneous EQ-5D and SF-6D Group Mean Scores
Bibliographic record
Abstract
BACKGROUND: Group mean estimates and their underlying distributions are the focus of assessment for cost and outcome variables in economic evaluation. Research focusing on the comparability of alternative preference-based measures of health-related quality of life has typically focused on analysis of individual-level data within specific clinical specialties or community-based samples. PURPOSE: To explore the relationship between group mean scores for the EQ-5D and SF-6D across the utility scoring range. METHODS: Studies were identified via a systematic search of 13 online electronic databases, a review of reference lists of included papers, and hand searches of key journals. Studies were included if they reported contemporaneous mean EQ-5D and SF-6D health state scores. All (sub)group comparisons of group mean EQ-5D and SF-6D scores identifiable from text, tables, or figures were extracted from identified studies. A total of 921 group mean comparisons were extracted from 56 studies. The nature of the relationship between the paired scores was examined using ranked scatter graphs and analysis of agreement. RESULTS: Systematic differences in group mean estimates were observed at both ends of the utility scale. At the lower (upper) end of the scale, the SF-6D (EQ-5D) provides higher mean utility estimates. CONCLUSIONS: These findings show that group mean EQ-5D and SF-6D scores are not directly comparable. This raises serious concerns about the cross-study comparability of economic evaluations that differ in the choice of preference-based measures, although the review focuses on 2 of the available instruments only. Further work is needed to address the practical implications of noninterchangeable utility estimates for cost-per-QALY estimates and 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 | 0.049 | 0.256 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.023 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".