EQ-5D and SF-36 Quality of Life Measures in Systemic Lupus Erythematosus: Comparisons with Rheumatoid Arthritis, Noninflammatory Rheumatic Disorders, and Fibromyalgia
Bibliographic record
Abstract
OBJECTIVE: The Medical Outcomes Study Short-form 36 (SF-36) provides numerical measurement of patient health, but does not include preferences for health states and cannot be used directly in cost-effectiveness analyses. By contrast the Euroqol EQ-5D can be used for cost-effectiveness analyses. The EQ-5D has rarely been used in systemic lupus erythematosus (SLE). We compared SF-36 and EQ-5D values across rheumatic diseases. METHODS: We studied 1316 patients with SLE, 13,722 with rheumatoid arthritis (RA), 3623 with non-inflammatory rheumatic disorders (NIRD), and 2733 with fibromyalgia (FM). RESULTS: The mean EQ-5D, physical (PCS) and mental (MCS) component summary scores were 0.72, 36.3, and 44.3, respectively, in SLE. There was essentially no difference among EQ-5D and PCS scores for patients with SLE, RA, or NIRD. MCS was lower in SLE compared with RA and NIRD (44.3, 49.1, 50.8, respectively). All scores were more abnormal in FM (0.61, 31.9, 41.9). Within SF-36 domains, physical function was better, but general health, vitality, social function, role-emotional, and mental health were more impaired in SLE compared with RA and NIRD. In SLE, quality of life (QOL) was predicted by damage, comorbidity, income, education, and age. Fifteen percent of patients with SLE were very satisfied with their health, and their QOL scores (0.84, 45.4, 50.1) were similar to those found in the US population for EQ-5D and MCS, but were slightly reduced for PCS. CONCLUSION: EQ-5D and PCS are at the same levels in SLE as in RA and NIRD, but are more abnormal in SLE in the MCS and mental health domains. EQ-5D values allow preference-based comparisons with other chronic conditions.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".