Safety and efficacy of additional courses of rituximab in patients with active rheumatoid arthritis: An open‐label extension analysis
Bibliographic record
Abstract
OBJECTIVE: To determine the safety and efficacy of additional courses of rituximab in patients with rheumatoid arthritis (RA). METHODS: An open-label extension analysis of RA patients previously treated with rituximab was conducted. Patients who had participated in any of 3 double-blind trials were eligible for additional courses (2 infusions of 1,000 mg given 2 weeks apart) if they exhibited a swollen joint count and tender joint count of > or =8 with > or =16 weeks elapsing after the previous course. Safety was assessed in patients receiving all or a portion of a rituximab course. Efficacy was assessed 24 weeks after each course, using the American College of Rheumatology 20% criteria for improvement (ACR20), ACR50, ACR70, European League Against Rheumatism (EULAR) response criteria, Disease Activity Score in 28 joints, the disability index of the Health Assessment Questionnaire, and Short Form 36 scores, stratified according to prior tumor necrosis factor (TNF) inhibitor exposure. RESULTS: A total of 1,039 patients received > or =1 course of rituximab. Of these, 570 received 2 courses, 191 received 3 courses, and 40 received 4 courses, for a total of 1,669 patient-years. Irrespective of prior TNF inhibitor exposure, ACR20 responses were comparable at week 24 after course 1 and at week 24 after course 2 (65% versus 72%), as were ACR50 and ACR70 responses. EULAR moderate/good responses were also comparable in course 2 relative to course 1 (88% versus 79%), with EULAR remission occurring in a 2-fold higher proportion of patients after course 2 than after course 1 (13% versus 6%). The most common adverse events, which were mild-to-moderate acute infusion-related events, decreased with each course. The serious infection rate after course 1 (5.1 per 100 patient-years) remained stable through additional courses. The proportion of patients with circulating IgM and IgG levels below the lower limit of normal (LLN) increased with subsequent courses; however, serious infection rates in these patients (5.6 per 100 patient-years in patients with low IgM levels and 4.8 per 100 patient-years in patients with low IgG levels were comparable with those in patients with immunoglobulin levels above the LLN (4.7 per 100 patient-years). Patients with human antichimeric antibody (9.2%) did not exhibit decreasing efficacy or present additional safety concerns. CONCLUSION: These findings indicate that patients treated with repeated courses of rituximab have sustained clinical responses with no new adverse events.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".