Chronic Conditions and Health Problems in Rheumatic Diseases: Comparisons with Rheumatoid Arthritis, Noninflammatory Rheumatic Disorders, Systemic Lupus Erythematosus, and Fibromyalgia
Bibliographic record
Abstract
OBJECTIVES: To describe and compare the prevalence of lifetime and current self-reported comorbidity and associated quality of life in 4 rheumatic diseases, and to investigate comorbid conditions in light of the overlap between the index condition and comorbid conditions (CC), and in the context of symptom-type diagnoses. METHODS: We studied comorbidity in 11,704 patients with fibromyalgia (FM), systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and noninflammatory rheumatic disorders (NIRD). Patients completed semiannual self-reports relating to 22 present and past illnesses and completed the EuroQol (EQ-5D) utility index. RESULTS: CC were most common in FM, followed by SLE. FM comorbidity was dominated by depression, mental illness, and symptom-type comorbidity (e.g., gastrointestinal and genitourinary disorders). In SLE, there were substantial increases in hypertension, depression, cataract, fractures, and cardiovascular and cerebrovascular, neurologic, lung, gall bladder and endocrine disorders compared with RA. Any current CC reduced the EQ-5D utility by 0.08 to 0.16 units. The lowest EQ-5D score was noted for current psychiatric illness (0.55) and current depression (0.60). CONCLUSION: Four patterns of comorbidity emerged: that associated with aging; that associated with aging but enhanced by the index condition, as in SLE and cardiovascular disease; comorbidity that is part of the symptoms complex of the index condition; and CC that represent lifetime traits or manifestations of the underlying illness. Depression was the most strongly associated correlate of EQ-5D quality of life, and current depression was present in about 15% of patients with RA or NIRD and 34% to 39% of those with SLE and FM.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".