Rituximab Therapy for Systemic Rheumatoid Vasculitis: Indications, Outcomes, and Adverse Events
Bibliographic record
Abstract
OBJECTIVE: To characterize the indication, outcomes, and adverse effects of rituximab (RTX) treatment in a large single-center cohort of patients with systemic rheumatoid vasculitis (RV). METHODS: We retrospectively reviewed the medical charts of 17 patients treated with RTX for systemic RV from 2000 to 2017. Clinical characteristics, outcomes, and adverse effects were analyzed. RESULTS: At RV diagnosis, mean age was 59 years, 59% were female, 94% were white, and 82% had positive rheumatoid factor. At the time of initiating RTX, median Birmingham Vasculitis Activity Score for rheumatoid arthritis was 4.0 (interquartile range 2.0-7.5). RV presented in the skin in 8 patients (47%), as mononeuritis multiplex in 2 (12%), inflammatory ocular disease in 2 (12%), and affected multiple organ systems in 5 (29%). RTX was used for induction therapy in 8 patients (47%), relapsing RV in 4 (24%), second-line therapy in 2 (12%), and salvage therapy or in combination with another agent in 3 (18%). At 3 months, 2 (13%) of 15 patients with available followup information achieved complete remission (CR), and 10 (67%) achieved partial response (PR). At 6 months, 6 patients (40%) achieved CR, 8 (53%) achieved PR, and one had no response. At 12 months, 8 of 13 patients with available records (62%) had CR and 5 patients (38%) had PR. CONCLUSION: Systemic RV is difficult to treat effectively. CR of RV was achieved in 62% and PR in 38% of patients within 12 months of RTX use. Further evidence is needed to inform treatment for patients with RV.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".