Maintenance of Patient-Reported Outcomes in Baricitinib-Treated Patients with Moderate-to-Severe Active Rheumatoid Arthritis: Post Hoc Analyses from Two Phase 3 Trials
Bibliographic record
Abstract
INTRODUCTION: Baricitinib has been shown to improve patient-reported outcomes (PROs) in patients with rheumatoid arthritis (RA) who are inadequate responders (IR) to conventional synthetic and biologic disease-modifying antirheumatic drugs (csDMARDs and bDMARDs, respectively). We assessed the ability of baricitinib 2-mg to maintain minimal clinically important differences (MCIDs) in PROs until week 24 among week 4 and 12 responders. METHODS: Data were from two phase 3 trials, RA-BUILD (NCT01721057; csDMARD-IR patients) and RA-BEACON (NCT01721044; bDMARD-IR patients). PROs included Pain Visual Analogue Scale, Health Assessment Questionnaire-Disability Index, Functional Assessment of Chronic Illness Therapy-Fatigue, Short-Form 36 Physical Component Score, and Patient's Global Assessment of Disease Activity. Outcomes were evaluated by proportions of patients achieving MCID improvements, number needed to treat (NNT) at weeks 4, 12, and 24, proportions of patients maintaining MCID responses at week 24 among week 4 or 12 responders, and median time to achieve substantial response with baricitinib 2-mg versus placebo. RESULTS: A higher proportion of baricitinib-treated patients achieved MCID improvements, with NNTs ranging from 5 to 8 for baricitinib 2-mg versus placebo at week 24. Generally, early MCID responses in PROs at weeks 4 or 12 were better maintained through week 24 in RA patients treated with baricitinib 2-mg versus placebo. Patients treated with baricitinib 2-mg also achieved substantial PRO responses or normative values more quickly than placebo. CONCLUSIONS: These results suggest baricitinib-treated patients with RA achieving MCID improvement in PROs at weeks 4 and 12 maintained those improvements over time and that substantial PRO responses were achieved quickly.
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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 | 0.026 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".