Comparison of EuroQol-5D and Short Form-6D Utility Scores in Multiethnic Asian Patients with Psoriatic Arthritis: A Cross-sectional Study
Bibliographic record
Abstract
OBJECTIVE: To compare EuroQol-5D (EQ-5D) and Short Form-6D (SF-6D) utility scores in multiethnic Asian patients with psoriatic arthritis (PsA). METHODS: Consecutive patients fulfilling the Classification Criteria for Psoriatic Arthritis attending a rheumatology outpatient clinic were recruited and completed the EQ-5D and SF-6D questionnaires. Comparisons were performed by score distribution, mean, median, and the Outcome Measures in Rheumatology filter: i.e., truth, discrimination, and feasibility. RESULTS: Eighty-six patients were enrolled (69 English-speaking and 17 Chinese-speaking; male:female ratio 0.91). The score distribution of SF-6D was normal, while that of EQ-5D was bimodal. A ceiling effect was observed in 20% of patients for EQ-5D and none for SF-6D. There were moderate correlations (Spearman's rho = 0.59, p < 0.0001) between the 2 scores, but poor agreements on scatterplot, intraclass correlation (ICC 0.43 and standardized ICC 0.21), and Bland-Altman plots. EQ-5D generated lower utility scores than SF-6D in the poorer health subgroup. SF-6D had stronger correlation with the general health status and other external measures of health; and it distinguished better between good and poor general health status, with better effect size and relative efficiency statistics. EQ-5D demonstrated higher patient acceptability. CONCLUSION: EQ-5D and SF-6D instruments generated different utility scores in PsA. SF-6D may be superior because of normal scaling distribution and the absence of ceiling and floor effects. SF-6D also had better construct validity and better discrimination of poor health status. More studies are required for cost-utility analysis in PsA.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".