Indirect Treatment Comparison of Abatacept with Methotrexate Versus Other Biologic Agents for Active Rheumatoid Arthritis Despite Methotrexate Therapy in the United Kingdom
Bibliographic record
Abstract
OBJECTIVE: To compare the efficacy of abatacept and alternative biologic disease-modifying antirheumatic drugs (DMARD) in patients with rheumatoid arthritis (RA) and an inadequate response to methotrexate (MTX) in the United Kingdom. METHODS: A systematic literature search identified 11 individual studies investigating the efficacy of abatacept, infliximab, adalimumab, etanercept, certolizumab pegol, and golimumab in adult patients with RA that did not respond to MTX. The clinical trials included in this analysis were similar in trial design, baseline patient characteristics, and background therapy (i.e., MTX). The key clinical endpoints of interest were the Health Assessment Questionnaire (HAQ) change from baseline (CFB) and the American College of Rheumatology (ACR) responses at 6 months (24-28 weeks). Results were analyzed using Bayesian network metaanalysis methods, and were expressed as differences in HAQ CFB and ACR20/50/70 relative risks, with 95% credible limits (CrL). RESULTS: Analysis of HAQ CFB at 6 months showed that abatacept is more efficacious than placebo [mean difference in HAQ CFB: -0.30 (95% CrL -0.42; -0.16)] and comparable to all other biologic agents, in patients receiving MTX as background treatment. Abatacept is also expected to result in a higher proportion of ACR responders compared to placebo, with relative risks ranging from 1.90 (95% CrL 1.24; 2.57) for ACR20 to 3.72 (95% CrL 1.50; 10.52) for ACR70, and to result in comparable proportions of ACR responders as other biologic agents, at 6 months. CONCLUSION: Abatacept is expected to result in improvement in functional status comparable to other recommended biologic agents in patients with RA who are unresponsive to MTX in the UK.
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.021 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".