ReviewFrontiers in pediatrics2023
Refining empiric subgroups of pediatric sepsis using machine-learning techniques on observational data.
Review in Frontiers in pediatrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Proteomic profiling and pathway analyses reveal molecular signatures and immune networks in pediatric sepsis.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2026Article
- Role of biomarkers in pediatric sepsis: What evidence says?World journal of clinical pediatrics · 2026Review
- Article
- Screening and Identification of Neutrophil Extracellular Trap-related Diagnostic Biomarkers for Pediatric Sepsis by Machine Learning.Inflammation · 2025Article
- Sepsis subphenotypes: bridging the gaps in sepsis treatment strategies.Frontiers in immunology · 2025Review
- Review
- Deciphering the molecular classification of pediatric sepsis: integrating WGCNA and machine learning-based classification with immune signatures for the development of an advanced diagnostic model.Frontiers in genetics · 2024Article
- Exploring disease axes as an alternative to distinct clusters for characterizing sepsis heterogeneity.Intensive care medicine · 2023Article
- Clinical Sepsis Phenotypes in Critically Ill Patients.Microorganisms · 2023Review
- Lysosome-Related Diagnostic Biomarkers for Pediatric Sepsis Integrated by Machine Learning.Journal of inflammation research · 2023Article
- Editorial: Methods in Pediatric Critical Care 2022.Frontiers in pediatrics · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Sepsis contributes to 1 of every 5 deaths globally with 3 million per year occurring in children. To improve clinical outcomes in pediatric sepsis, it is critical to avoid "one-size-fits-all" approaches and to employ a precision medicine approach. To advance a precision medicine approach to pediatric sepsis treatments, this review provides a summary of two phenotyping strategies, empiric and machine-learning-based phenotyping based on multifaceted data underlying the complex pediatric sepsis pathobiology. Although empiric and machine-learning-based phenotypes help clinicians accelerate the diagnosis and treatments, neither empiric nor machine-learning-based phenotypes fully encapsulate all aspects of pediatric sepsis heterogeneity. To facilitate accurate delineations of pediatric sepsis phenotypes for precision medicine approach, methodological steps and challenges are further highlighted.
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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 OpenQuestion graph.