Comparative Efficacy of Tumor Necrosis Factor-α Inhibitors in Ankylosing Spondylitis: A Systematic Review and Bayesian Network Metaanalysis
Bibliographic record
Abstract
OBJECTIVE: To compare the efficacy of 6 tumor necrosis factor-α inhibitors (TNFi) in treatment of ankylosing spondylitis (AS) at 12 weeks and 24 weeks. METHODS: We performed a systematic literature review of randomized controlled trials of TNFi in patients with active AS. We included trials that reported efficacy at 10 to 14 weeks (12-week analysis) and at 24 to 30 weeks (24-week analysis). We used Bayesian network metaanalysis (NMA) to compare their relative efficacy to improve the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), Bath Ankylosing Spondylitis Functional Index (BASFI), and C-reactive protein (CRP) level. RESULTS: We included 20 trials of 6 TNFi, with 43 treatment arms and 3220 participants. All TNFi were significantly better than placebo in reducing BASDAI and BASFI at 12 weeks and 24 weeks; all but certolizumab pegol (CZP) were statistically better than placebo in reducing CRP at 12 weeks; all but CZP and infliximab-dyyb (IFX biosimilar) were significantly better than placebo in reducing CRP at 24 weeks. IFX was superior to the other TNFi in decreasing BASDAI at 12 weeks, but not at 24 weeks. Excluding 1 open-label trial, there were no differences among TNFi. CONCLUSION: Based on this NMA of clinical trials, IFX was superior to other TNFi in reducing BASDAI at 12 weeks, but sensitive to inclusion of an open-label trial, and its efficacy was diminished at 24 weeks. The analysis was limited by few direct comparison trials. Further study of relative safety and longterm effectiveness will help inform the choice of TNFi in treating active AS.
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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 | 0.038 | 0.069 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".