Abatacept in difficult-to-treat juvenile idiopathic arthritis
Bibliographic record
Abstract
Jasmin B Kuemmerle-Deschner1, SM Benseler21Pediatric Rheumatology Clinics, Dept of Pediatrics, University Hospital Tübingen, Germany; 2Dept of Pediatric Rheumatology, The Hospital for Sick Children, Toronto, CanadaAbstract: Juvenile idiopathic arthritis (JIA) is the most common chronic rheumatic disease in children and an important cause of short-term and long-term disability. Gene changes in the immune system can predispose to JIA and regulation of the immune system is crucial in the pathogenesis. The goal of therapy is complete disease control using disease-modifying antirheumatic drugs (DMARDS). Activated T-cells may play a role in the immunopathology of JIA. Therefore, targeting T-cell activation is a rational approach for the treatment of JIA. Abatacept (ABA), a selective co-stimulation modulator, has been shown to be effective in treating all JIA subtypes and is generally safe and well tolerated in JIA. Neutralizing antibodies were found in 6/9 (67%) of seropositive patients, but anti-ABA antibodies did not appear to be associated with disease flare, serious adverse events, acute infusional adverse events, hypersensitivity, autoimmune disorders, or low ABA serum concentrations. Anti-ABA antibodies were more frequent when ABA concentrations were below therapeutic levels. Although information on ABA in JIA is still limited, available data suggest a potential role in difficult to treat JIA patients previously treated with other biologic agents and for non-responders to TNF-blockade.Keywords: abatacept, juvenile idiopathic arthritis (JIA), biologics
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".