Effectiveness of a Third Tumor Necrosis Factor-α-blocking Agent Compared with Rituximab After Failure of 2 TNF-blocking Agents in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To compare the effectiveness of a third tumor necrosis factor-α (TNF-α)-blocking agent with rituximab after failure of 2 TNF-blocking agents in patients with rheumatoid arthritis (RA) in daily clinical practice. METHODS: Patients receiving a third TNF-blocking agent or rituximab after failure of 2 TNF-blocking agents were selected from a Dutch biologic registry. The primary outcome was the results from the Disease Activity Score of 28 joints (DAS28) over the first 12 months after start of the third biologic using mixed-model analyses. Secondary outcomes included the course of the Health Assessment Questionnaire (HAQ) and the separate components of the DAS28 over the first 12 months and the change from baseline in DAS28 and HAQ at 3 and 6 months. RESULTS: The overall course of the DAS28 over the first 12 months was significantly better for rituximab (p = 0.0044), as also observed for the HAQ, although the latter results were not statistically significant (p = 0.0537). The erythrocyte sedimentation rates, C-reactive protein, and swollen joint counts showed a better course for rituximab (p = 0.0008, p = 0.0287, p = 0.0547, respectively), but not the tender joint counts or visual analog scale for general health. DAS28 decreased significantly in both groups at 3 and 6 months (p ≤ 0.024), but the change in HAQ was significant for rituximab only at 3 months (p = 0.009). CONCLUSION: During the first 12 months of therapy, a larger improvement in disease activity and a trend toward a larger decrease in functional disability was observed in patients receiving rituximab. Switching to a biologic with another mechanism of action might be more effective after failure of 2 TNF-blocking agents in RA.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".