Treatment Patterns of Multimorbid Patients with Rheumatoid Arthritis: Results from an International Cross-sectional Study
Bibliographic record
Abstract
OBJECTIVE: To describe the treatment profile of multimorbid patients with rheumatoid arthritis (RA) in contrast to patients with RA only. METHODS: COMORA (Comorbidities in Rheumatoid Arthritis) is a cross-sectional, international study assessing morbidities, outcomes, and treatment of patients with RA. Patients were grouped according to their multimorbidity profile assessed by a counted multimorbidity index (cMMI). Treatment for RA was categorized as use of biologic disease-modifying antirheumatic drugs (bDMARD), in particular tumor necrosis factor inhibitors (TNFi), synthetic DMARD (sDMARD) use only, nonsteroidal antiinflammatory drug (NSAID) use, and corticosteroid use. Logistic regression models were performed to determine the OR of bDMARD, TNFi, sDMARD, NSAID, or corticosteroid use based on a patient's cMMI and global region after adjusting for age, disease activity, disease duration, educational level, and previous DMARD therapy. RESULTS: Out of 3920 patients, 32.7% received bDMARD; 59.9% sDMARD only, 51.1% used concomitant NSAID, and 54.8% used corticosteroid. Regional differences were observed with the most frequent use of bDMARD in the United States (46.5%) and lowest in North Africa (9%). After adjusting for confounders in logistic regression, the OR for bDMARD use was reduced for each additional morbidity (OR 0.89, 95% CI 0.83-0.96). Similar results were found for TNFi (OR 0.91, 95% CI 0.84-0.99), whereas the OR for use of sDMARD was increased (1.13, 95% CI 1.05-1.22). No significant change of OR was found for NSAID or corticosteroid use. CONCLUSION: In this study, the odds of bDMARD use decreases 11% for each additional chronic morbid condition after adjustment for regional differences, disease activity, and other covariates.
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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 | 0.000 | 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".