Sex Differences in Rheumatoid Arthritis: New Insights From Clinical and Patient-Reported Outcome Perspectives
Bibliographic record
Abstract
Objective The disease course and burden of rheumatoid arthritis (RA) may differ between female and male individuals, but existing data on these differences are limited and often contradictory. Therefore, we investigated whether clinical outcomes and patient-reported outcomes (PROs) differ between female and male patients with RA over time. Methods All female (n = 286) and male (n = 139) patients with RA according to 1987 and/or 2010 criteria from Treatment in the Rotterdam Early Arthritis Cohort (tREACH), a stratified single-blinded trial with a treat-to-target (T2T) approach and fixed medication protocol, were included. Clinical outcomes include disease activity, medication usage, sustained disease-modifying antirheumatic drug (DMARD)-free remission (SDFR), and radiographic progression. In addition, the following PROs were investigated: general health, pain, functional ability, health-related quality of life, fatigue, productivity loss, and a possible depression or anxiety disorder. For comparisons over time, a mixed model or Cox proportional hazard model was used. The mixed models were adjusted for age, initial treatment, and disease activity (Disease Activity Score in 44 joints [DAS44]). Results Female patients had a higher DAS44 over time compared to male patients (β 0.36, 95% CI 0.25-0.47, P < 0.001), which also resulted in more treatment adjustments including use of biologic DMARDs (bDMARDs; 36% vs 24%, P < 0.001). Although not significant, first bDMARD survival seemed shorter in female patients (hazard ratio [HR] 1.4, 95% CI 0.8-2.6, P = 0.24). However, no differences were found in SDFR and radiographic progression. With regard to PROs, only functional ability differed significantly between sexes after adjusting for confounders, including disease activity (Health Assessment Questionnaire–Disability Index, β 0.10, 95% CI 0.04-0.17, P < 0.001). Conclusion Clinical outcomes and PROs are intertwined, and both improve with a T2T management approach. Nevertheless, female patients with RA have higher disease activity, a greater need for bDMARDs—although these have lower efficacy—and more functional impairment over time, underscoring the need for sex-specific management recommendations. (Trial registration number: ISRCTN26791028 )
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".