The incidence and risk factors of uveitis in children with juvenile idiopathic arthritis (JIA): a meta -analysis and literature review
Bibliographic record
Abstract
BACKGROUND: Uveitis is a serious complication of juvenile idiopathic arthritis (JIA). Despite its seriousness, a comprehensive understanding of its incidence and early risk factors remains elusive. This knowledge gap poses challenges for formulating tailored clinical early identification and prevention strategies. Therefore, our study aims to review the incidence and risk factors of uveitis in JIA patients, and provide evidence-based insights for developing specific clinical risk identification and prevention strategies. METHODS: We systematically searched databases including PubMed, Cochrane, Embase, and Web of Science until December 31, 2023. The quality of included studies was assessed through the Newcastle-Ottawa Scale (NOS). Incidence data were synthesized from cohort studies, and meta-analysis was conducted through R language. RESULTS: Our review encompassed 28 original studies involving 22,834 JIA patients, among whom 3,381 developed uveitis during the follow-up period. Meta-analysis revealed an overall prevalence of uveitis at 12.7% (95% CI: 10.5 - 15.1%), with rates of 14.3% (95% CI: 11.9 - 15.1%) in European populations, 6.5% (95% CI: 4.0 - 9.5%) in Asian populations, and 13.4% (95% CI: 9.5 - 17.8%) in North America. Identified risk factors for the development of uveitis included early age at JIA onset, ANA-positive, and increased ESR. CONCLUSION: The notable prevalence of uveitis in JIA demands clinical vigilance. Our study findings highlight that age, ANA status, and ESR correlate with risk of complicated uveitis. Future research endeavors could focus on constructing a concise risk assessment tool incorporating more potent independent factors. Such a tool would enhance screening efficacy within this demographic, facilitating tailored preventive strategies.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.043 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".