Effectiveness of a Second Biologic After Failure of a Non–tumor Necrosis Factor Inhibitor As First Biologic in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: In rheumatoid arthritis (RA), evidence regarding the effectiveness of a second biologic disease-modifying antirheumatic drug (bDMARD) in patients whose first-ever bDMARD was a non-tumor necrosis factor inhibitor (TNFi) bDMARD is limited. The objective of this study was therefore to assess the outcome of a second bDMARD (non-TNFi: rituximab [RTX], abatacept [ABA], or tocilizumab [TCZ], separately; and TNFi) after failure of a non-TNFi bDMARD as first bDMARD. METHODS: We identified patients with RA from the 5 Nordic biologics registers who started treatment with a non-TNFi as first-ever bDMARD but switched to a second bDMARD. For the second bDMARD, we assessed drug survival (at 6 and 12 months) and primary response (at 6 months). RESULTS: We included 620 patients starting a second bDMARD (ABA 86, RTX 40, TCZ 67, and TNFi 427) following failure of a first non-TNFi bDMARD. At 6 and 12 months after start of their second bDMARD, approximately 70% and 60%, respectively, remained on treatment, and at 6 months, less than one-third of patients were still on their second bDMARD and had reached low disease activity or remission according to the Disease Activity Score in 28 joints. For those patients whose second bMDARD was a TNFi, the corresponding proportion was slightly higher (40%). CONCLUSION: The drug survival and primary response of a second bDMARD in patients with RA switching due to failure of a non-TNFi bDMARD as first bDMARD is modest. Some patients may benefit from TNFi when used after failure of a non-TNFi as first bDMARD.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".