Effect of Glucocorticoids on the Clinical and Radiographic Efficacy of Tofacitinib in Patients with Rheumatoid Arthritis: A Posthoc Analysis of Data from 6 Phase III Studies
Bibliographic record
Abstract
OBJECTIVE: Tofacitinib has been investigated for the treatment of rheumatoid arthritis (RA) in phase III studies in which concomitant glucocorticoids (GC) were allowed. We analyzed the effect of GC use on efficacy outcomes in patients with RA receiving tofacitinib and/or methotrexate (MTX) or conventional synthetic disease-modifying antirheumatic drugs (csDMARD) in these studies. METHODS: Our posthoc analysis included data from 6 phase III studies (NCT01039688; NCT00814307; NCT00847613; NCT00853385; NCT00856544; NCT00960440). MTX-naive patients or patients with inadequate response to csDMARD or biological DMARD received tofacitinib 5 or 10 mg twice daily alone or with csDMARD, with or without concomitant GC. Patients receiving GC (≤ 10 mg/day prednisone or equivalent) before enrollment maintained a stable dose throughout. Endpoints included the American College of Rheumatology (ACR) 20/50/70 response rates, rates of Clinical Disease Activity Index (CDAI)-defined low disease activity (LDA; CDAI ≤ 10) and remission (CDAI ≤ 2.8), and changes from baseline in CDAI, 28-joint count Disease Activity Score (DAS28-4)-erythrocyte sedimentation rate (ESR), Health Assessment Questionnaire-Disability Index (HAQ-DI), pain visual analog scale (VAS), and modified total Sharp score. RESULTS: Of 3200 tofacitinib-treated patients, 1258 (39.3%) received tofacitinib monotherapy and 1942 (60.7%) received tofacitinib plus csDMARD; 1767 (55.2%) received concomitant GC. ACR20/50/70 response rates, rates of CDAI LDA and remission, and improvements in CDAI, DAS28-4-ESR, HAQ-DI, and pain VAS with tofacitinib were generally similar with or without GC in monotherapy and combination therapy studies. GC use did not appear to affect radiographic progression in tofacitinib-treated MTX-naive patients. MTX plus GC appeared to inhibit radiographic progression to a numerically greater degree than MTX alone. CONCLUSION: Concomitant use of GC with tofacitinib did not appear to affect clinical or radiographic efficacy. MTX plus GC showed a trend to inhibit radiographic progression to a greater degree than MTX alone.
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 | 0.030 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".