Longterm Safety and Efficacy of Adalimumab and Infliximab for Uveitis Associated with Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
OBJECTIVE: Anti-TNF-α agents have significantly changed the management of juvenile idiopathic arthritis (JIA). We evaluated the safety and efficacy of adalimumab (ADA) and infliximab (IFX) for the treatment of JIA-associated uveitis in patients treated for ≥ 2 years. METHODS: Patients with JIA-associated uveitis treated with IFX and ADA were managed by a standardized protocol and data were entered in the ORCHIDEA registry. At baseline, all patients were refractory to standard immunosuppressive treatment or were corticosteroid-dependent. Data recorded every 3 months were uveitis course, number/type of ocular flares and complications, drug-related adverse events (AE), and treatment switch or withdrawal. Data of patients treated for ≥ 2 years were analyzed by descriptive statistics. RESULTS: Up to December 2014, 154 patients with ≥ 24 months followup were included in the study. Fifty-nine patients were treated with IFX and 95 with ADA. Clinical remission, defined as the absence of flares for > 6 months on treatment, was achieved in 69 patients (44.8%), with a better remission rate for ADA (60.0%) as compared to IFX (20.3%; p < 0.001). A significant reduction of flares was observed in all patients without difference between the 2 treatment modalities. The number of new ocular complications decreased in both groups but was lower for ADA (p = 0.015). No serious AE were recorded; 16.4% of patients experienced 35 minor AE and the incidence rate was lower with ADA than with IFX. CONCLUSION: At the 2-year followup, ADA showed a better efficacy and safety profile than IFX for the treatment of refractory JIA-associated uveitis.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".