Prevalence and characteristics of adults with difficult-to-treat rheumatoid arthritis in a large patient registry
Bibliographic record
Abstract
OBJECTIVES: An estimated 5-20% of patients with rheumatoid arthritis (RA) fail multiple treatments and are considered 'difficult-to-treat' (D2T), posing a substantial clinical challenge for rheumatologists. A European League Against Rheumatism (EULAR) task force proposed a definition of D2T-RA in 2021. We applied EULAR's D2T definition in a cohort of patients with established RA to assess prevalence, and we compared clinical characteristics of participants with D2T-RA with matched comparisons. METHODS: Data from the longitudinal Brigham and Women's Hospital Rheumatoid Arthritis Sequential Study (BRASS) registry were used. Participants were classified as D2T if they met EULAR's definition. A comparison group of non-D2T-RA patients were matched 2:1 to every D2T patient, and differences in characteristics were evaluated in descriptive analyses. Prevalence rates of D2T were estimated using Poisson regression. RESULTS: We estimated the prevalence of D2T-RA to be 14.4 (95% CI: 12.8, 16.3) per 100 persons among 1581 participants with RA, and 22.3 (95% CI: 19.9, 25.0) per 100 persons among 1021 who were biologic/targeted synthetic DMARD experienced. We observed several differences in demographics, comorbidities and RA disease activity between D2T-RA and non-D2T-RA comparisons. Varying EULAR sub-criteria among all participants in BRASS resulted in a range of D2T-RA prevalence rates, from 0.6 to 17.5 per 100 persons. CONCLUSION: EULAR's proposed definition of D2T-RA identifies patients with RA who have not achieved treatment targets. Future research should explore heterogeneity in these patients and evaluate outcomes to inform the design of future studies aimed at developing more effective RA management protocols.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".