Evidence map›Paper›PMID 41204129›Full record

ArticleBMC infectious diseases2025

Novel screening model for infectious mononucleosis in febrile pediatric patients using a 3D-DIFF scattergram.

Zhaoyang Peng, Jingxian Zhang, Lin Chen, Kesheng Li, Fengqing Cai, Wenbo Zheng, Lisu Huang, Baohai Chen, Jing Zou, Chunda Wang and 4 more

Abstract read
In one paragraph

Article in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

14 authors.

Zhaoyang Peng *Department of Laboratory Medicine, Children's Hospital Zhejiang University School of Medicine, Hangzhou, 310052, China.
Jingxian Zhang *Clinical Department (IVD), Shenzhen Mindray Bio-Medical Electronics Co., Ltd, Shenzhen, 518057, China.
Lin ChenDepartment of Laboratory Medicine, Children's Hospital Zhejiang University School of Medicine, Hangzhou, 310052, China.
Kesheng LiClinical Department (IVD), Shenzhen Mindray Bio-Medical Electronics Co., Ltd, Shenzhen, 518057, China.
Fengqing CaiDepartment of Laboratory Medicine, Children's Hospital Zhejiang University School of Medicine, Hangzhou, 310052, China.
Wenbo ZhengClinical Department (IVD), Shenzhen Mindray Bio-Medical Electronics Co., Ltd, Shenzhen, 518057, China.
Lisu HuangDepartment of Infectious Disease, Children's Hospital Zhejiang University School of Medicine, Hangzhou, 310052, China.
Baohai ChenDepartment of Data and Information, Children's Hospital Zhejiang University School of Medicine, Hangzhou, 310052, China.
Jing ZouClinical Department (IVD), Shenzhen Mindray Bio-Medical Electronics Co., Ltd, Shenzhen, 518057, China.
Chunda WangClinical Department (IVD), Shenzhen Mindray Bio-Medical Electronics Co., Ltd, Shenzhen, 518057, China.
Xiaoxia GaoDepartment of Laboratory Medicine, Northwest Women's and Children's Hospital, Xi'an, 710061, China.
Bin HuangDepartment of Laboratory Medicine, Northwest Women's and Children's Hospital, Xi'an, 710061, China.
Hongqiang ShenDepartment of Laboratory Medicine, Children's Hospital Zhejiang University School of Medicine, Hangzhou, 310052, China. shenhq@zju.edu.cn.
Gang YuDepartment of Data and Information, Children's Hospital Zhejiang University School of Medicine, Hangzhou, 310052, China. yugbme@zju.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInfectious mononucleosis (IM) is an acute self-limited disease caused mainly by Epstein-Barr virus (EBV) that is prone to be missed or misdiagnosed in febrile children due to a lack of obvious clinical symptoms. Establishing a model based on blood cell parameters for identifying patients with IM is important for the subsequent diagnosis and treatment of children with fever.

methodsFrom January to December 2023, data of a total of 5511 febrile children (including 518 IM patients with primary EBV infection) were collected from Children's Hospital Zhejiang University School of Medicine. After assessing the changes in lymphoid clusters on the 3D-DIFF scatter plot and further analyzing the parameters of the scattergrams for quantifying the fluorescence signal changes in IM patients through the probability density function, eleven features were selected from among 41 blood cell parameters to construct the final Novel IM screening model (Novel IMs model).

resultsThe Novel IMs model performed well in three sets, screening IM patients with an accuracy of more than 91.92%. Compared others, the Novel IMs model had the best identification accuracy, specificity, and sensitivity and reached an area under the receiver operating characteristic (ROC) curve (AUC) in identifying IM patients of 0.96.

conclusionThis study is the first to develop a machine learning model to screening IM patients from febrile children using parameters obtained from blood cell scattergrams. This convenient and low-cost technique for initial screening of IM by hematology analysis may improve the screening coverage of the disease and further reduce misdiagnosis and underdiagnosis.

Indexed as

FeverInfectious MononucleosisMass ScreeningChildChild, PreschoolFemaleHerpesvirus 4, HumanHumansInfantMachine LearningMaleROC CurveSensitivity and SpecificityBlood cell parametersFebrile pediatric patientsInfectious mononucleosis diseasesMachine learning

Identifiers

PMID41204129
PMCPMC12595630

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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.