Persistence and Dose Escalation of Tumor Necrosis Factor Inhibitors in US Veterans with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Limited evidence exists comparing the persistence, effectiveness, and costs of biologic therapies for rheumatoid arthritis in clinical practice. Comparative effectiveness studies are needed to understand real-world experience with these agents. We evaluated treatment patterns, costs, and effectiveness of tumor necrosis factor inhibitor (TNFi) agents in patients enrolled in the Veterans Affairs Rheumatoid Arthritis (VARA) registry. METHODS: Observational data from the VARA registry and linked administrative databases were analyzed. Longitudinal data from VARA patients initiating adalimumab (ADA), etanercept (ETN), or infliximab (IFX) from 2003 (the date all agents were available within the Veteran Affairs) to 2010 were analyzed. Outcomes included Disease Activity Score using 28 joints (DAS28), treatment persistence, dose escalation, and direct costs of drugs and drug administration. RESULTS: For 563 eligible patients, baseline DAS28, DAS28 improvements, and persistence on initial treatment were similar across agents. Fewer patients receiving ETN (n = 5/290; 2%) underwent dose escalation than did patients taking ADA (n = 32/204; 16%) or IFX (n = 44/69; 64%). Annual costs for first course of TNFi therapy were lower for injectable ADA ($13,100 US) and ETN ($13,500 US) than for intravenously administered IFX ($16,900 US). CONCLUSION: Despite similar persistence and clinical disease activity for these TNFi agents, rates of dose escalation were highest with ADA and IFX. Higher overall costs were noted for IFX without increases in effectiveness.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".