Patient-reported outcomes of baricitinib in patients with rheumatoid arthritis and no or limited prior disease-modifying antirheumatic drug treatment
Bibliographic record
Abstract
BACKGROUND: This study evaluates patient-reported outcomes (PROs) in a double-blind, phase III study of baricitinib as monotherapy or combined with methotrexate (MTX) in patients with active rheumatoid arthritis (RA) with no or minimal prior conventional synthetic disease-modifying antirheumatic drugs (DMARDs) and naïve to biological DMARDs. METHODS: Patients were randomized 4:3:4 to MTX administered once weekly (N = 210), baricitinib monotherapy (4 mg once daily (QD), N = 159), or combination of baricitinib (4 mg QD) and MTX (baricitinib + MTX, N = 215). PROs included the Patient's Global Assessment of Disease Activity (PtGA), patient's assessment of pain, Health Assessment Questionnaire-Disability Index (HAQ-DI), Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F), duration of morning joint stiffness (MJS), worst joint pain, worst tiredness, Work Productivity and Activity Impairment-Rheumatoid Arthritis (WPAI-RA), Short Form 36 version 2, Acute (SF-36); and EuroQol 5-Dimensions (EQ-5D) Health State Profile. Comparisons were assessed with analysis of covariance (ANCOVA) and logistic regression models. RESULTS: Compared to MTX, patients in both baricitinib groups reported greater improvement (p ≤ 0.01) in HAQ-DI, PtGA, pain, fatigue, worst join pain, SF-36 physical component score, and EQ-5D at weeks 24 and 52. For the SF-36 mental component score, patients in both baricitinib groups reported statistically significant improvements (p ≤ 0.01) at week 52 compared to MTX-treated patients. Statistically significant improvements (p ≤ 0.05) were observed with the WPAI-RA for the baricitinib groups vs. MTX at week 24 and for the WPAI-RA daily activity and work productivity measures for baricitinib + MTX at week 52. CONCLUSIONS: In this study, baricitinib alone or in combination with MTX, when used as initial therapy, resulted in significant improvement compared to MTX in the majority of the pre-specified PRO measures. TRIAL REGISTRATION: ClinicalTrials.gov, NCT01711359 . Registered on 18 October 2012.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".