ArticleiScience2023
Similarity network fusion to identify phenotypes of small-for-gestational-age fetuses.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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.
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Who cites it
6 citing papers in PubMed, 6 citations in OpenAlex.
- Phenotyping Preeclampsia Using Unsupervised Machine Learning: A Prospective Cohort Study.BJOG : an international journal of obstetrics and gynaecology · 2026Article
- Inflammation at the Maternal-Fetal Interface: Mechanisms Linking Maternal-Fetal Immunity to Preeclampsia and Fetal Growth Restriction.International journal of molecular sciences · 2026Review
- Novel Unsupervised Machine Learning Model Using Multi-Parametric Radiomics for Prognostic Stratification of Bifocal Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2026Article
- Maternal stress and fetoplacental cortisol regulation in small-for-gestational-age newborns.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2025Article
- Analysis of Umbilical Artery Hemodynamics in Development of Intrauterine Growth Restriction Using Computational Fluid Dynamics with Doppler Ultrasound.Bioengineering (Basel, Switzerland) · 2024Article
- Review
Corrections and comments
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Authors and funding
15 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fetal growth restriction (FGR) affects 5-10% of pregnancies, is the largest contributor to fetal death, and can have long-term consequences for the child. Implementation of a standard clinical classification system is hampered by the multiphenotypic spectrum of small fetuses with substantial differences in perinatal risks. Machine learning and multiomics data can potentially revolutionize clinical decision-making in FGR by identifying new phenotypes. Herein, we describe a cluster analysis of FGR based on an unbiased machine-learning method. Our results
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Registered trials
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 Socratic graph.