Evidence for Tocilizumab as a Treatment Option in Refractory Uveitis Associated with Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
OBJECTIVE: To report on experience using the anti-interleukin 6 receptor antibody tocilizumab (TCZ) to treat severe and therapy-refractory uveitis associated with juvenile idiopathic arthritis (JIA). METHODS: Retrospective data were gathered from patients with JIA receiving TCZ treatment for uveitis. JIA and related uveitis data (disease onset, activity, structural complications, and topical and systemic antiinflammatory treatment) were evaluated at the start of TCZ (baseline) and every 3 months during TCZ therapy. RESULTS: A total of 17 patients (14 women) with active uveitis were included (mean age 15.3 ± 6.9 yrs, mean followup time 8.5 mos). In all patients, uveitis had been refractory to previous topical and systemic corticosteroids, methotrexate (MTX), and other synthetic and biological disease-modifying antirheumatic drugs, including ≥ 1 tumor necrosis factor-α (TNF-α) inhibitor. Uveitis inactivity was achieved in 10 patients after a mean of 5.7 months of TCZ treatment (in 3 of them, it recurred during followup) and persisted in the remaining 7 patients. By using TCZ, systemic corticosteroids or immunosuppressives could be spared in 7 patients. Macular edema was present in 5 patients at baseline and improved in all of them under TCZ treatment. Arthritis was active in 11 patients at the initial and in 6 at the final followup visit. CONCLUSION: TCZ appears to represent a therapeutic option for severe JIA-associated uveitis that has been refractory to MTX and TNF-α inhibitors in selected patients. The present data indicate that inflammatory macular edema responds well to TCZ in patients with 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".