Effectiveness of Rituximab in Patients with Rheumatoid Arthritis: Observational Study from the British Society for Rheumatology Biologics Register
Bibliographic record
Abstract
OBJECTIVE: To assess the effectiveness of rituximab (RTX) in patients with rheumatoid arthritis (RA) in routine clinical practice, and to identify predictors of 6-month response to RTX in patients for whom at least 1 anti-tumor necrosis factor-α (anti-TNF) therapy has failed. METHOD: The analysis involved 646 patients with RA registered with the British Society for Rheumatology Biologics Register (BSRBR) who were starting RTX and were followed for at least 6 months. Change in the 28-joint Disease Activity Score (DAS28), European League Against Rheumatism (EULAR) response, and proportions of patients achieving disease remission were used to assess the clinical response 6 months after starting RTX. Regression analyses were used to identify factors associated with the response in the patients for whom anti-TNF therapy had not worked. The models included baseline demographics, disease characteristics, baseline Health Assessment Questionnaire (HAQ), and drug history including biologic history. RESULTS: The mean DAS28 at baseline was 6.2 (95% CI 6.1, 6.3), which decreased significantly to 4.8 (95% CI 4.7, 4.9) at the 6-month followup. Seventeen percent of the patients were EULAR good responders and 43% were moderate responders. Eight percent of the patients achieved disease remission. Subjects with higher baseline DAS28 score and those with positive rheumatoid factor (RF) status were significantly associated with a decrease in their DAS28 score (improvement), while women and patients with higher baseline HAQ score were less likely to improve. CONCLUSION: RTX has proven to be effective in routine clinical practice. When anti-TNF therapy fails, response to RTX was influenced by baseline DAS28 score, RF status, baseline HAQ score, and sex.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| 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".