Tumour necrosis factor α inhibitors in the treatment of childhood uveitis
Bibliographic record
Abstract
OBJECTIVE: To describe the efficacy of anti-TNF-alpha agents in the treatment of childhood uveitis. METHODS: We performed a retrospective chart review of all children with uveitis treated with TNF-alpha blockers at The Hospital for Sick Children, Toronto. RESULTS: Twenty-one children with uveitis were treated with the anti-TNF-alpha agents etanercept (11 patients) and infliximab (13 patients), resulting in 24 treatment courses. All patients had persistently active uveitis despite treatment with at least one standard immunosuppressive drug before the start of anti-TNF-alpha therapy. Six of 21 patients (29%) had idiopathic uveitis. In the other 15 patients, the underlying disease was juvenile idiopathic arthritis in 12 (57%), Behçet disease in two (9%) and sarcoidosis in one (5%). Response to etanercept treatment was good in 27%, moderate in 27% and poor in 45% of patients. Response to infliximab treatment was good in 38%, moderate in 54% and poor in 8% of patients. The difference in the percentage of patients with a moderate or good response was statistically significant (P = 0.0481). We also observed a lower rate of complications, such as new-onset or worsening glaucoma or cataract in the infliximab-treated group. CONCLUSION: Anti-TNF-alpha treatment was beneficial in a high percentage of patients with childhood uveitis refractory to standard immunosuppressive treatment. Infliximab resulted in better clinical responses with less ocular complications than etanercept.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".