Safety and Efficacy of Infliximab and Adalimumab for Refractory Uveitis in Juvenile Idiopathic Arthritis: 1-year Followup Data from the Italian Registry
Bibliographic record
Abstract
OBJECTIVE: To evaluate safety and efficacy of adalimumab (ADA) and infliximab (IFX) for the treatment of juvenile idiopathic arthritis-related anterior uveitis (JIA-AU). METHODS: Starting January 2007, patients with JIA-AU treated with IFX and ADA were managed by a standard protocol and data were entered into the National Italian Registry (NIR). At baseline, all patients were refractory to standard immunosuppressive treatment and/or were corticosteroid-dependent. Data recorded every 3 months included uveitis course, number/type of ocular complications, drug-related adverse events (AE), treatment change or withdrawal, and laboratory measures. Data of patients treated for at least 1 year were retrieved from the NIR and analyzed using descriptive statistics. Treatment efficacy was based on change in uveitis course and in number of ocular complications. RESULTS: Up to December 2009, data for 108 patients with JIA-AU treated with anti-tumor necrosis factor-α agents were recorded in the NIR and data from 91, with at least 12 months' followup, were included in the study. Forty-eight patients were treated with IFX, 43 with ADA. Forty-seven patients (55.3%) achieved remission of AU, 28 (32.9%) had recurrent AU, and 10 (11.8%) maintained a chronic course. A higher remission rate was observed with ADA (67.4% vs 42.8% with IFX; p = 0.025). Ocular complications decreased from 0.47 to 0.32 per subject. Five patients experienced resolution of structural complications. No patient reported serious AE; 8 (8.8%) experienced 11 minor AE (9 with IFX, 2 with ADA). CONCLUSION: IFX and ADA appear to be effective and safe for treatment of refractory JIA-related uveitis, with a better performance of ADA in the medium-term period.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".