Etanercept Versus Monoclonal Antibodies in Axial Spondyloarthritis: Game Over?
Bibliographic record
Abstract
More than 10 years of experience with anti-tumor necrosis factor-α (TNF-α) therapy in ankylosing spondylitis (AS) has brought us new therapeutic players. As respectively infliximab (IFX), etanercept (ETN), adalimumab, golimumab, and certolizumab were introduced, we were confronted with new modes of administration. However, regarding efficacy on axial and peripheral manifestations, all 5 actors seem somewhat equipotent. Also, IFX, ETN, adalimumab, and golimumab have already shown longterm efficacy after 2 to 8 years in established AS1,2,3,4. Nevertheless, treatment with TNF-α blocking agents such as adalimumab and ETN has shown similar high efficacy, with up to 54.5% ASAS40 (Assessment of Spondyloarthritis international Society 40%) response in early disease5,6. In this issue of The Journal , Song, et al report results on the longterm efficacy of ETN in patients with early axial spondyloarthritis (axSpA)7. Generally speaking, 2 types of TNF-α therapy are distinguished: ETN, a soluble TNF-α receptor antagonist, and monoclonal antibodies. All TNF blocking agents display similar short-term efficacy in axSpA4,8,9,10,11,12 (Figure 1). Adalimumab displayed sustained clinical efficacy after 2 years of treatment with an ASAS40 response of 39.4% at Week 24 to an ASAS40 response in 50.6% after 2 years2,10. Also IFX maintained ASAS20 response of 84.8% after 8 years of treatment13. Similar results can be found in ETN and golimumab after 2 years3,4. However, all of these followup studies have been published in AS, generally comprising patients with longer disease duration and more … Address correspondence to Dr. Elewaut, University of Ghent, Rheumatology, De Pintelaan 185, Ghent 9000 Belgium; E-mail: dirk.elewaut{at}ugent.be
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".