Switching between Janus kinase inhibitor upadacitinib and adalimumab following insufficient response: efficacy and safety in patients with rheumatoid arthritis
Bibliographic record
Abstract
OBJECTIVES: To evaluate efficacy and safety of immediate switch from upadacitinib to adalimumab, or vice versa, in patients with rheumatoid arthritis with non-response or incomplete-response to the initial therapy. METHODS: SELECT-COMPARE randomised patients to upadacitinib 15 mg once daily (n=651), placebo (n=651) or adalimumab 40 mg every other week (n=327). A treat-to-target study design was implemented, with blinded rescue occurring prior to week 26 for patients who did not achieve at least 20% improvement in both tender and swollen joint counts ('non-responders') and at week 26 based on Clinical Disease Activity Index (CDAI) >10 ('incomplete-responders') without washout. RESULTS: A total of 39% (252/651) and 49% (159/327) of patients originally randomised to upadacitinib and adalimumab were rescued to the alternate therapy. In both switch groups (adalimumab to upadacitinib and vice versa) and in non-responders and incomplete-responders, improvements in disease activity were observed at 3 and 6 months following rescue. CDAI low disease activity was achieved by 36% and 47% of non-responders and 45% and 58% of incomplete-responders switched to adalimumab and upadacitinib, respectively, 6 months following switch. Overall, approximately 5% of rescued patients experienced worsening in disease activity at 6 months postswitch. The frequency of adverse events was similar between switch groups. CONCLUSIONS: These observations support a treat-to-target strategy, in which patients who fail to respond initially (or do not achieve sufficient response) are switched to a therapy with an alternate mechanism of action and experience improved outcomes. No new safety findings were observed despite immediate switch without washout.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".