Comparative Persistence of the TNF Antagonists in Rheumatoid Arthritis – A Population-Based Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To compare persistence with tumor necrosis factor alpha (TNF) antagonists among rheumatoid arthritis patients in British Columbia. Treatment persistence has been suggested as a proxy for real-world therapeutic benefit and harm of treatments for chronic non-curable diseases, including rheumatoid arthritis. We hypothesized that the different pharmacological characteristics of infliximab, adalimumab and etanercept cause statistically and clinically significant differences in persistence. METHODS: We conducted a population-based cohort study using administrative health data from the Canadian province of British Columbia. The study cohort included rheumatoid arthritis patients who initiated the first course of a TNF antagonist between 2001 and 2008. Persistence was measured as the time between first dispensing to discontinuation. Drug discontinuation was defined as a drug-free interval of 180 days or switching to another TNF antagonist, anakinra, rituximab or abatacept. Persistence was estimated and compared using survival analysis. RESULTS: The study cohort included 2,923 patients, 63% treated with etanercept. Median persistence in years (95% confidence interval) with infliximab was 3.7 (2.9-4.9), with adalimumab 3.3 (2.6-4.1) and with etanercept 3.8 (3.3-4.3). Similar risk of discontinuation was observed for the three drugs: the hazard ratio (95% confidence interval) was 0.98 (0.85-1.13) comparing infliximab with etanercept, 0.95 (0.78-1.15) comparing infliximab with adalimumab and 1.04 (0.88-1.22) comparing adalimumab with etanercept. CONCLUSIONS: Similar persistence was observed with infliximab, adalimumab and etanercept in rheumatoid arthritis patients during the first 9 years of use. If treatment persistence is a good proxy for the therapeutic benefit and harm of these drugs, then this finding suggests that the three drugs share an overall similar benefit-harm profile in rheumatoid arthritis patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".