Response of Pediatric Uveitis to Tumor Necrosis Factor-α Inhibitors
Bibliographic record
Abstract
OBJECTIVE: To evaluate the outcome of tumor necrosis factor-α inhibition (anti-TNF) for pediatric uveitis. METHODS: We retrospectively assessed children (age ≤ 18 yrs) with noninfectious uveitis receiving anti-TNF at 5 uveitis centers and 1 pediatric rheumatology center. Incident treatment success was defined as minimal or no uveitis activity at ≥ 2 consecutive ophthalmological examinations ≥ 28 days apart while taking no oral and ≤ 2 eyedrops/day of corticosteroids. Eligible children had active uveitis and/or were taking higher corticosteroid doses. RESULTS: Among 56 eligible children followed over 33.73 person-years, 52% had juvenile idiopathic arthritis (JIA) and 75% had anterior uveitis (AU). The Kaplan-Meier estimated proportion achieving treatment success within 12 months was 75% (95% CI 62%-87%). Complete absence of inflammatory signs with discontinuation of all corticosteroids was observed in an estimated 64% by 12 months (95% CI 51%-76%). Diagnoses of JIA or AU were associated with greater likelihood of success, as was the oligoarticular subtype among JIA cases. In a multivariable model, compared to those with JIA-associated AU, those with neither or with JIA or AU alone had a 75%-80% lower rate of achieving quiescence under anti-TNF, independent of the number of immunomodulators previously or concomitantly prescribed. Uveitis reactivated within 12 months of achieving quiescence in 14% of those continuing anti-TNF (95% CI 6%-31%). The incidence of discontinuation for adverse effects was 8%/year (95% CI 1%-43%). CONCLUSION: Treatment with anti-TNF was successful and sustained in a majority of children with noninfectious uveitis, and treatment-limiting toxicity was infrequent. JIA-associated AU may be especially responsive to anti-TNF.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".