Changes in Market Share of Biologic and Targeted Synthetic Disease-Modifying Anti-Rheumatic Drugs for Treatment of Rheumatoid Arthritis: Results from the Ontario Best-Practice Research Initiative Database
Bibliographic record
Abstract
OBJECTIVE: For patients with Rheumatoid Arthritis (RA) who do not achieve adequate clinical response with combined conventional synthetic disease-modifying anti-rheumatic drugs (cs- DMARDs), initiation of advanced therapies such as biologic DMARDs (bDMARDs) or targeted synthetic DMARDs (tsDMARDs) is recommended. Tumour necrosis factor inhibitors (TNFi) are the oldest and most commonly used subgroup of advanced therapies. In the last decade, new non-TNFi advanced therapy options have become available. We described the relative use of TNFi vs. non-TNFi in Ontario-based practices from 2008-2017. METHODS: Adult patients with RA enrolled in the Ontario Best Practices Research Initiative (OBRI) database who started bDMARDs or tsDMARDs anytime during or within 30 days prior to enrollment were included. The proportion of patients treated with TNFi vs. non-TNFi agents between 2008 and 2017 was described for all patients and those initiating their first bDMARD/tsDMARD. All TNFi therapies were included. Non-TNFi included Abatacept, Rituximab, Tocilizumab, and Tofacitinib. RESULTS: A total of 1,057 patients were included, of whom 72.0% were bDMARD/tsDMARD naïve. In 2008, the relative non-TNFi use was 5.4% in all patients while it was 0% in bDMARD/ts- DMARD-naïve patients. In 2017, the proportion of patients using non-TNFi increased to 33.8% among all patients and 33.3% in bDMARD/tsDMARD-naïve patients. CONCLUSION: This descriptive analysis of data from the OBRI cohort reveals that TNFi are still used in the majority of cases; however, there has been an increase in the use of non-TNFi therapies both overall and as first-line advanced therapy. This trend towards non-TNFi therapies as first-line advanced therapy may be partially explained by the shift in guideline recommendations from TNFi as first-line to any of the advanced therapeutics.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| 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) | 0.003 | 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".