<scp>Real‐World</scp> Treatment and Care Patterns in Patients With Rheumatoid Arthritis Initiating <scp>First‐Line</scp> Tumor Necrosis Factor Inhibitor Therapy in the United States
Bibliographic record
Abstract
OBJECTIVE: Treatment guidelines for rheumatoid arthritis (RA) recommend targeting low disease activity or remission and switching therapies for patients not reaching those targets. We evaluated real-world use of disease activity measures, treatment discontinuation, and switching patterns among patients with RA initiating a first-line tumor necrosis factor inhibitor (TNFi). METHODS: Data from adult patients with RA initiating a first-line TNFi were collected from the American Rheumatology Network (January 2014-August 2021). The proportion of patients with recorded disease activity scores (Clinical Disease Activity Index [CDAI] or Routine Assessment of Patient Index Data 3 [RAPID3]) at TNFi initiation was assessed. Among patients with moderate or severe RA at TNFi initiation, reasons for discontinuation and subsequent advanced therapy were evaluated. RESULTS: Among TNFi initiators (n = 15,182), 44.8% recorded a CDAI/RAPID3 score at treatment initiation; of those who did not, 47.0% had recorded a tender and/or swollen joint count or pain score. Among patients with moderate or severe RA (n = 1,651), 52% discontinued their initial TNFi during follow-up, of which 15%, 46%, 28%, and 12% initiated the same TNFi, another TNFi, a non-TNFi biologic, or a Janus kinase inhibitor, respectively. The proportion of patients restarting the same TNFi or initiating another TNFi varied according to TNFi discontinuation reason. CONCLUSION: In clinical practice, over half of patients with RA initiating a first-line TNFi did not have baseline disease activity assessments. Many patients cycled through TNFi despite citing lack of efficacy as the most common reason for discontinuation. Consistent, objective monitoring of treatment response and timely switch to effective therapy is needed in patients with RA.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".