Longterm Followup of Rituximab Therapy in Patients with Rheumatoid Arthritis: Results from the Belgian MabThera in Rheumatoid Arthritis Registry
Bibliographic record
Abstract
OBJECTIVE: Our study reports the results of the MIRA (MabThera In Rheumatoid Arthritis) registry, set up to collect data about clinical usage, patient profile, and retention of rituximab (RTX) treatment in daily clinical practice in Belgium. METHODS: Patients with active rheumatoid arthritis (RA) who failed at least 1 anti-tumor necrosis factor (anti-TNF) treatment were included in our study between November 2006 and October 2011. At baseline, demographics, medication, disease history, disease activity, rheumatoid factor (RF), and anticyclic citrullinated peptide antibodies (anti-CCP) status were recorded. Evolution of the 28-joint Disease Activity Score (DAS28)-erythrocyte sedimentation rate, retreatments, and reasons for therapy stop were followed prospectively. RESULTS: The MIRA registry included 649 patients, with mean disease duration of 12.8 ± 0.4 years and DAS28 values at inclusion of 5.85 ± 0.48. Patients received on average 2.82 ± 0.07 (range 1-9) RTX treatments, over a mean followup period of 93.1 ± 2.6 weeks. At database lock, 433 patients (66.7%) were still under RTX treatment, 182 (28.0%) had stopped treatment, and 34 (5.2%) were lost to followup. Ineffectiveness (n = 108, 59%) and safety concerns (n = 39, 22%) were the most frequent reasons for discontinuing RTX therapy. From 2006 to 2011, RTX practice patterns clearly evolved toward RTX being started in patients with a lower number of previously failed anti-TNF drugs and lower baseline DAS28 values. A lower number of previous anti-TNF drugs, and positivity for RF and anti-CCP, predicted more successful longterm treatment. RTX treatment provided adequate longterm disease control. CONCLUSION: In our daily practice study, RTX provided good longterm disease control and treatment retention in refractory patients with RA. Over the years, rheumatologists tended to start this treatment in patients with fewer previous anti-TNF treatments and lower disease activity.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".