Treatment Patterns and Effectiveness of Tofacitinib in Patients Initiating Therapy for Rheumatoid Arthritis: Results From the CorEvitas Rheumatoid Arthritis Registry
Bibliographic record
Abstract
OBJECTIVE: This real-world analysis assessed baseline demographics/characteristics and treatment patterns/effectiveness in patients with rheumatoid arthritis (RA) initiating tofacitinib (TOF) in the US CorEvitas RA Registry. METHODS: The primary analysis of this study included patients with RA initiating TOF with a 12-month follow-up visit from November 2012 to January 2021. Outcomes included baseline demographics/characteristics and TOF initiation/discontinuation reasons, treatment patterns, and effectiveness (disease activity and patient-reported outcomes [PROs] at 12 months); the primary effectiveness outcome was Clinical Disease Activity Index low disease activity (CDAI LDA). All data, analyzed descriptively, were stratified by TOF regimen (monotherapy vs combination therapy), line of therapy (second- to fourth-line), time of initiation (2012-2014, 2015-2017, or 2018-2020), and dose (5 mg twice daily vs 11 mg once daily). RESULTS: Of 2874 patients with RA who initiated TOF, 1298 had a qualifying 12-month follow-up visit; of these, 43.1% were monotherapy and 66.5% were fourth-line therapy. Overall, tumor necrosis factor inhibitors (40.8%) were the most common treatment immediately prior to TOF initiation. The most common reason for TOF initiation (among those with a reason) was lack/loss of efficacy of prior treatment (67.7%). Overall, at 12 months, 31.9% and 10.1% had achieved CDAI LDA and remission, respectively; 22.4%, 10.4%, and 5% had achieved ≥ 20%, ≥ 50%, and ≥ 70% improvement in modified American College of Rheumatology core set measures, respectively; and improvements in PROs were observed. Effectiveness was generally similar across TOF stratifications. CONCLUSION: TOF effectiveness (CDAI LDA) was observed in a US real-world setting of patients with RA regardless of TOF regimen, line of therapy, time of initiation, and dose. (ClinicalTrials.gov: NCT04721808).
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".