Evidence map›Paper›PMID 41219742›Full record

ArticlePediatric rheumatology online journal2025

Machine learning for screening and predicting the risk of developing uveitis in juvenile idiopathic arthritis.

Li Li, Junmei Zhang, Jianghong Deng, Weiying Kuang, Xiaohua Tan, Chao Li, Shipeng Li, Caifeng Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Pediatric rheumatology online journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Li LiDepartment of Rheumatology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China.
Junmei ZhangDepartment of Rheumatology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China.
Jianghong DengDepartment of Rheumatology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China.
Weiying KuangDepartment of Rheumatology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China.
Xiaohua TanDepartment of Rheumatology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China.
Chao LiDepartment of Rheumatology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China.
Shipeng LiDepartment of Rheumatology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China.
Caifeng LiDepartment of Rheumatology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China. caifeng_li@yeah.net.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveEstablishing a predictive model using clinical indicators for the early identification of JIA-U.

methodA cross-sectional study was conducted with 255 patients admitted at Beijing Children's Hospital between 2018 and 2023. The model was fitted using stepwise logistic regression as well as least absolute shrinkage and selection operator (LASSO) regression. Calibration and decision curve analysis were used for validation.

resultsThe final predictive model included four clinical variables (patient's gender, age at onset, arthritis subtype, and ANA status). A nomogram for risk prediction was developed, which demonstrated good discrimination in both the training cohort (AUC = 0.8417; 95% CI = 0.775-0.9085) and the testing cohort (AUC = 0.782; 95% CI = 0.6752-0.8884). Calibration curves showed that, through bootstrap resampling, the nomogram performed well in predicting the occurrence of uveitis in JIA.

conclusionThis study established a predictive model using routine clinical indicators to assess the risk of uveitis in JIA patients.

Indexed as

Arthritis, JuvenileMachine LearningUveitisAdolescentChildChild, PreschoolCross-Sectional StudiesFemaleHumansMaleNomogramsRisk AssessmentRisk FactorsJuvenile idiopathic arthritisMachine learningRisk factorsUveitis

Identifiers

PMID41219742
PMCPMC12606941

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.