ArticleFrontiers in pediatrics2024
Developing and evaluating a predictive model for neonatal hyperbilirubinemia based on
Article in Frontiers in pediatrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.
- Association of SLCO1B1 genetic variants with neonatal hyperbilirubinemia: a consolidated analysis of 36 studies.BMC pediatrics · 2025Pooled it
- TabPFN-based explainable machine learning for predicting neonatal hyperbilirubinemia: A decade-long retrospective cohort study.Medicine · 2026Article
- Factors associated with neonatal jaundice among neonates admitted to three hospitals in Burao, Somaliland: a facility-based unmatched case-control study.BMC pediatrics · 2025Article
- Genetic variation features of neonatal hyperbilirubinemia caused by inherited diseases.World journal of clinical pediatrics · 2024Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Neonatal hyperbilirubinemia (NHB) is one of the most common diseases in the neonatal period. Without timely diagnosis and treatment, it can lead to long-term complications. In severe cases, it may even result in fatality. The Methods: In this study, we conducted a cohort study and analyzed 3258 newborns from the Jilin Women And Children Health Hospital in northern China, including 372 children with hyperbilirubinemia. We established a predictive model using a logistic regression model based on clinical risk factors and the polymorphism of the G211A locus in the Results: The logistic regression model indicates that the following factors are associated with an increased risk of NHB: the time when stool turns yellow [ Conclusion: We have developed and evaluated a predictive model that combines
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Registered trials
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