Utilization Trends of Tumor Necrosis Factor Inhibitors Among Patients with Rheumatoid Arthritis in a United States Observational Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Studies have suggested that early institution of tumor necrosis factor (TNF) inhibitors improves functional status and slows radiographic progression among patients with rheumatoid arthritis (RA). To determine whether these findings have altered practice patterns, we used the Consortium of Rheumatology Researchers of North America (CORRONA) registry to assess the pattern of TNF inhibitor utilization in the US over time. METHODS: Demographics and disease activity data were collected for patients with RA. The trend of TNF inhibitor use during 2002-06 was evaluated prospectively using linear and logistic regression models. RESULTS: Of the 11,397 patients with RA, 66% and 34% had established RA and early RA (disease duration < 3 yrs), respectively. The majority of patients were female and Caucasian. Despite comparable levels of disease activity, more of the patients with established RA were taking TNF inhibitors than those with early RA (40% vs 25%; p < 0.0001). The majority of patients (70%) taking TNF inhibitors were also receiving disease modifying antirheumatic drugs. The use of TNF inhibitors increased at a rate of 2.8% per year in established RA and 1.2% per year in early RA. The mean Clinical Disease Activity Index at the start of TNF inhibitors decreased at a rate of -0.233 per quarter (p = 0.006), while the mean Disease Activity Score decreased at a rate of -0.04 per quarter (p = 0.022). CONCLUSION: Utilization of TNF inhibitors in this multicenter, observational US cohort is increasing in both early and established RA, although it is more prominent among patients with established RA. The level of disease activity at which TNF inhibitors were initiated decreased over time in patients with both established and early 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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".