Is There a Future for Interleukin 17 Blocking Agents in Rheumatoid Arthritis?
Bibliographic record
Abstract
Rheumatoid arthritis (RA) is a chronic inflammatory disease characterized by synovitis leading to the progressive destruction of the joints and irreversible disability. The pathophysiology of the disease is complex with various groups of immune and joint cells, soluble mediators, and autoantibodies identified to participate in the pathogenesis. Different animal models have provided a better understanding of the altered immune functions and regulation in RA. In such models, recent research has focused on Th17 cells and interleukin (IL) 17 production to demonstrate a central role in the synovitis and the destruction of joint and bone1. However, the extrapolation of the immunopathology of RA animal models to human diseases remains controversial. In recent years, our knowledge about the pathomechanisms of RA has expanded to such a degree that specific therapies targeting various cells or soluble mediators of inflammation have been developed. Tumor necrosis factor-α (TNF-α) has been largely described in the literature as a key player in RA, but the ongoing demonstration of the involvement of other mediators gives us alternative treatment options. T cells are another important target in autoimmune diseases such as RA, as confirmed by the effective use of abatacept, an agent that selectively blocks T cell costimulation2. Recently, studies in RA have demonstrated that T cells, including Th17 cells, infiltrate the joints and that IL-17 promotes osteoclastogenesis, suggesting an appropriate target3 … Address correspondence to Dr. P. Durez; E-mail: Patrick.Durez{at}uclouvain.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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".