Baricitinib Dose Reduction in Patients With Rheumatoid Arthritis Achieving Sustained Disease Control: Final Results From the RA-BEYOND Study
Bibliographic record
Abstract
OBJECTIVE: This study examined the effect of dose step-down in patients with rheumatoid arthritis (RA) who achieved sustained disease control with baricitinib 4 mg once daily for up to 96 weeks. METHODS: Patients who completed a baricitinib phase III study could enter a long-term extension (LTE). In the LTE, patients who received baricitinib 4 mg for ≥ 15 months and maintained Clinical Disease Activity Index (CDAI) low disease activity (LDA) or remission (REM) were blindly randomized to continue with 4 mg or taper to 2 mg. If needed, 2-mg-treated patients could be rescued to 4 mg, and 4-mg-treated patients could be rescued by adding or increasing conventional synthetic disease-modifying antirheumatic drugs. Efficacy and safety were assessed through 96 weeks. Nonresponder imputation (NRI), considering rescued or discontinued patients as nonresponders, was used for CDAI response analyses. RESULTS: At 96 weeks, most patients maintained LDA in both 2-mg and 4-mg arms, with a lower maintenance rate in the 2-mg than the 4-mg group (NRI 59.9% and 70.2%, respectively). Patients maintained REM in the 2-mg and 4-mg arms at 30.8% and 36.6%, respectively. Rescue rates were 14.7% for baricitinib 4 mg and 22.5% for 2 mg. Of 112 patients who lost LDA in the 2-mg arm and were rescued to 4 mg, 76.2% and 75.6% achieved LDA again at 12 and 24 weeks postrescue, respectively. CONCLUSION: In a randomized, blinded, phase III LTE study, maintenance of disease control following induction of sustained LDA/REM with baricitinib 4 mg was greater with continued 4 mg than after tapering to 2 mg. Nonetheless, 76% of patients tapered to 2 mg could maintain LDA/REM or recapture with return to 4 mg, if needed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".