Real-world utilization of DMARDs and biologics in rheumatoid arthritis: the RADIUS (Rheumatoid Arthritis Disease-Modifying Anti-Rheumatic Drug Intervention and Utilization Study) study
Bibliographic record
Abstract
OBJECTIVE: Rheumatoid Arthritis (RA) Disease-Modifying Anti-Rheumatic Drug (DMARD) Intervention and Utilization Study (RADIUS) is a unique, real-world, prospective, 5-year, observational study of over 10 000 patients with RA. RADIUS provides a snapshot of use patterns, effectiveness, and safety of DMARDs, biologics, and combination therapies used to manage RA in clinical practice. RESEARCH DESIGN AND METHODS: Patients with RA requiring a new DMARD or biologic (addition or switch) were eligible for the RADIUS study. Two separate patient cohorts were enrolled; RADIUS 1 patients initiated any new therapy at entry, and RADIUS 2 patients initiated etanercept at entry. Patient demographics and disease activity measures were collected at study entry, and baseline characteristics were summarized for various subgroups. Effectiveness, safety, and patterns of use will be tracked for therapies utilized during the 5-year study. RESULTS: RADIUS 1 enrolled 4959 patients, and RADIUS 2 enrolled 5102 patients, mostly at community private practices (88%). In RADIUS 1, most patients initiated methotrexate (MTX) monotherapy, followed by MTX in combination with a biologic (e.g. infliximab plus MTX) or other DMARD. In RADIUS 2, most patients initiated etanercept in combination with MTX, followed by etanercept monotherapy. When a new therapy was required, physicians tended to add another therapy versus switching therapies. Patients initiating a biologic had a longer duration of RA and more severe disease compared with patients initiating non-biologic therapy. CONCLUSIONS: These real-world data provide evidence of the prescribing practices of rheumatologists in 2001-2003. Future analyses will allow evidence-based comparisons of the long-term safety and effectiveness of DMARDs, biologics, and combination therapies to assist physicians in clinical decision-making.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".