Prevalence of Co-existing Autoimmune Disease in Rheumatoid Arthritis: A Cross-Sectional Study
Bibliographic record
Abstract
INTRODUCTION: Many autoimmune diseases, including rheumatoid arthritis (RA), share common mechanisms; however, population-based studies of the magnitude of multiple autoimmune diseases in patients with RA have not been performed. METHODS: We conducted a cross-sectional study using a US administrative healthcare thcare claims database to screen for prevalence of multiple autoimmune diseases in patients with RA and osteoarthritis (OA). Each patient diagnosed with RA between January 1, 2006 and September 30, 2014 was age- and sex-matched with five patients with OA. The prevalence of 37 pre-specified autoimmune diseases during the 24-month period before and after RA or OA diagnosis was compared. RESULTS: Overall, 286,601 patients with RA and 992,838 matched patients (from 1,421,624 records) with OA were evaluated. During the baseline period, at least one and more than one autoimmune diseases were identified in 24.3% and 6.0% of patients with RA compared with 10.5% and 1.4% of patients with OA, respectively. Highest prevalence rates for patients with RA were for systemic lupus erythematosus (3.8% versus 0.7% for OA) and psoriatic arthritis (3.2% versus 0.4%). Highest odds ratios (ORs) comparing RA with OA were for the prevalence of ankylosing spondylitis (OR 8.0; 95% CI 7.6, 8.5) and psoriatic arthritis (OR 7.8; 95% CI 7.6, 8.1). CONCLUSION: Patients with RA have more concurrent autoimmune diseases than patients with OA. These data suggest that the interrelationship between RA and other autoimmune diseases, and outcomes associated with the occurrence of multiple autoimmune diseases, may play an important role in disease understanding, management, and treatment decisions. FUNDING: Bristol-Myers Squibb.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".