Treatment patterns and sequencing in patients with rheumatic diseases: a retrospective claims data analysis
Bibliographic record
Abstract
OBJECTIVES: Long-term real-world management of inflammatory rheumatic diseases remains unclear, especially with the advent of new treatment options. This study characterizes the number of advanced treatments used by patients with selected rheumatic diseases (rheumatoid arthritis [RA], psoriatic arthritis [PsA], ankylosing spondylitis, juvenile idiopathic arthritis) and provides a contemporary portrait of treatment patterns and therapeutic sequencing among patients with RA and PsA. METHOD: Patients were selected from a large US claims database and classified into disease subsamples based on the latest rheumatic diagnosis recorded before/on the day of initiation of the first advanced treatment (index date). The total number of advanced treatments was assessed within the first 5 years following the index date. Treatment patterns and therapeutic sequencing were assessed over the first 2 years. RESULTS: Approximately 20% of patients received ≥2 distinct advanced treatments during the first year following index date - the proportion increased to almost 50% among patients with 5 years of observation. Most patients (RA: 76.8%; PsA: 88.7%) initiated a tumor necrosis factor as the first advanced treatment. Over the first 2 years after the index date, 1/3 of RA and PsA patients switched to another advanced treatment. More than 50% initiated a second treatment with the same mechanism of action (MOA). A small proportion of patients received a biosimilar. CONCLUSION: Despite advent of treatments with different MOA, cycling between treatments with the same MOA was common. Further studies with longer data follow-up would be needed to assess the impact of higher adoption of biosimilars on treatment patterns/sequencing.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".