Persistence with Anti-Tumor Necrosis Factor Therapies in Patients with Rheumatoid Arthritis: Observations from the RADIUS Registry
Bibliographic record
Abstract
OBJECTIVE: To evaluate persistence with anti-tumor necrosis factor (TNF) therapy and predictors of discontinuation in patients with rheumatoid arthritis (RA). METHODS: This retrospective analysis used data from RADIUS 1, a 5-year observational registry of patients with RA, to determine time to first- and second-course discontinuation of etanercept, infliximab, and adalimumab. First-course therapy was defined as first exposure to anti-TNF therapy, and second-course therapy was defined as exposure to anti-TNF therapy after the first discontinuation. Kaplan-Meier survival analysis was used to assess persistence, log-rank tests were used to compare therapies, and Cox proportional hazards models were used to assess potential predictors of treatment discontinuation. RESULTS: This analysis included 2418 patients. Mean persistence rates were similar among treatments [first-course: etanercept, 51%; infliximab, 48%; adalimumab, 48% (followup was 54 weeks for etanercept and infliximab and 42 weeks for adalimumab); second-course: 56%, 50%, 46%, respectively (followup was 36 weeks for etanercept and infliximab and 30 weeks for adalimumab)]. Discontinuations of first-course therapy due to ineffectiveness were similar among treatments (etanercept, 19%; infliximab, 19%; adalimumab, 20%) and discontinuations due to adverse events were significantly (p = 0.0006) lower for etanercept than for infliximab (etanercept, 14%; infliximab, 22%; adalimumab, 17%). Predictors from univariable analysis of first- or second-course therapy discontinuation included increased comorbidities (etanercept), female sex (infliximab), Clinical Disease Activity Index > 22 (infliximab), and a Stanford Health Assessment Questionnaire score > 0.5 (adalimumab). CONCLUSION: In this population, first- and second-course persistence was similar among anti-TNF therapies. First-course discontinuation due to adverse events was lower with etanercept compared with infliximab.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".