A Comparison of Four Indirect Methods of Assessing Utility Values in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVES: Utility scores can be assessed indirectly using preference-based instruments and used as weightings for quality-adjusted life years in economic analyses. It is not clear whether available instruments yield similar results or what domains of health are contributing to the overall score in a sample of patients with rheumatoid arthritis (RA). SUBJECTS: Our study included 313 individuals with rheumatologist-confirmed RA. MEASURES: A self-completed survey that permitted scoring of 4 indirect utility instruments (the Health Utilities Index Mark 2 and 3 (HUI-2 and HUI-3), the EuroQoL (EQ-5D), and the Short Form 6D (SF-6D) was the basis of our study. RESULTS: Mean (standard deviation) global utility scores were 0.63 (0.24) for the SF-6D, 0.66 (0.13) for the EQ-5D, 0.71 (0.19) for the HUI-2, and 0.53 (0.29) for the HUI-3 (P = 0.02 by repeated-measures analysis of variance). The intraclass correlation across all the indices was 0.67 (95% confidence interval 0.62-0.71). Bland-Altman plots revealed that agreement among instruments was poor at lower utility values. In this elderly RA sample, all of the global utilities mostly measured functional ability and pain. CONCLUSIONS: There are significant differences in utilities obtained from different indirect methods. Agreement among the instruments was moderate but poorer at lower utilities. It is unlikely that these utility values, if used as the weightings for quality-adjusted life years, would result in comparable estimates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".