Effectiveness of Rituximab for the Treatment of Rheumatoid Arthritis in Patients with Prior Exposure to Anti-TNF: Results from the CORRONA Registry
Bibliographic record
Abstract
OBJECTIVE: To characterize the real-world effectiveness of rituximab (RTX) in patients with rheumatoid arthritis. METHODS: Clinical effectiveness at 12 months was assessed in patients who were prescribed RTX based on the Clinical Disease Activity Index (CDAI). Change in CDAI was calculated (CDAI at 12 mos minus at initiation). Achievement of remission or low disease activity (LDA; CDAI ≤ 10) among those with moderate/high disease activity at the time of RTX initiation was compared based on prior anti-tumor necrosis factor agent (anti-TNF) use (1 vs ≥ 2) using logistic regression models. RESULTS: Patients (n = 265) were followed for 12 months with a mean change in CDAI of -8.1 (95% CI -9.8 - -6.4). Of the 218 patients with moderate/high disease activity at baseline, patients with 1 prior anti-TNF (baseline CDAI 25.0) demonstrated a mean change in CDAI of -10.1 (95% CI -13.2 - -7.0); patients with ≥ 2 prior anti-TNF (baseline CDAI 30.0) demonstrated a mean change of -10.5 (95% CI -12.9 - -8.0). The unadjusted OR for achieving LDA/remission in patients with moderate/high disease activity at baseline exposed to ≥ 2 versus 1 prior anti-TNF was 0.40 (95% CI 0.22-0.73), which was robust to 4 different adjusted models (OR range 0.38-0.44). CONCLUSION: A good clinical response was observed in all patients; however, patients previously treated with 1 anti-TNF, who had lower baseline CDAI and a greater opportunity for clinical improvement compared with patients previously treated with ≥ 2 anti-TNF, were more likely to achieve LDA/remission.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".