Improving care for children with juvenile idiopathic arthritis: the role of IL-6 inhibitors in a patient-centered approach
Bibliographic record
Abstract
INTRODUCTION: Medical management of juvenile idiopathic arthritis (JIA) presents a significant challenge in pediatric rheumatology. Ideally, the treatment target is remission, though achieving this remains complex. Interleukin-6 (IL-6) inhibitors (IL-6is) play an important role, targeting the inflammatory pathways central to JIA pathogenesis. However, their optimal use is debated. AREAS COVERED: This narrative review examined JIA care needs and IL-6 inhibition. A SPIDER-based literature search of PubMed/MEDLINE, Semantic Scholar, WorldCat, Cochrane Library, Embase, CINAHL, ICTRP, and ClinicalTrials.gov (to May 2025), identifying 56 studies from 246 records published between 2018 and 2025. Key unmet needs include difficulty controlling the disease, diagnostic delay, shortcomings in biomarker research, and multidisciplinary support. Tocilizumab, a well-studied IL6i, showed efficacy in symptom reduction, disease control, and reduced glucocorticoid use. EXPERT OPINION: Addressing gaps in JIA management, such as delayed diagnosis and inadequate disease control, is essential. Experts advocate for early IL6i use within a treat-to-target framework, optimizing outcomes and minimizing glucocorticoid use. Recognizing benefits for highrisk JIA subtypes, experts support earlier tocilizumab integration into treatment algorithms, offering valuable options for refractory oligoarthritis and uveitis. Ultimately, bridging gaps in JIA management and reshaping real-world outcomes hinges on integrating clinical insight and research outcomes - a process driven by precision medicine.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".