Evidence map›Paper›PMID 41814232›Full record

ArticleBMC pediatrics2026

Determinants of expert-advised antibiotic indication in pediatric emergency department, data from a cross-sectional prevalence survey.

Man Jiang, Nan Zhang, Ping Song, Yingyi Chen, Jikui Deng, Jiaosheng Zhang

Abstract read
In one paragraph

Article in BMC pediatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Man Jiang *Department of Infectious Diseases, Shenzhen Children's Hospital, No. 7019 Yi Tian Road, Futian District, Shenzhen, 518038, China.
Nan Zhang *Department of Infectious Diseases, Shenzhen Children's Hospital, No. 7019 Yi Tian Road, Futian District, Shenzhen, 518038, China.
Ping SongPediatric Emergency Department, Shenzhen Children's Hospital, Shenzhen, 518038, China.
Yingyi ChenPediatric Emergency Department, Shenzhen Children's Hospital, Shenzhen, 518038, China.
Jikui DengDepartment of Infectious Diseases, Shenzhen Children's Hospital, No. 7019 Yi Tian Road, Futian District, Shenzhen, 518038, China.
Jiaosheng ZhangDepartment of Infectious Diseases, Shenzhen Children's Hospital, No. 7019 Yi Tian Road, Futian District, Shenzhen, 518038, China. zhjiaosheng@163.com.

Funding

Guangdong High-level Hospital Construction Fund Clinical Research Project of Shenzhen Children's Hospital LCYJ2022090Jiaosheng Zhang, Shenzhen Science and Technology Program JCYJ20250604180049064Sanming Project of Medicine in Shenzhen SZSM202311024Shenzhen Science and Technology Program JCYJ20220530155810024
6 · The paper itself

Abstract

objectiveAntibiotic resistance and inappropriate antibiotic use are critical global public health issues, but research on the determinants of expert-advised antibiotic prescribing in pediatric emergency departments (EDs) remains limited. This study aimed to quantify variability in expert antibiotic recommendatios for pediatric ED patients.

methodsA cross-sectional survey was conducted in a Chinese pediatric hospital ED in 2020. Two days/quarter (one weekday/weekend day, avoiding infectious disease outbreaks) were selected and 1000 cases were randomly extracted for secondary analysis. Clinical data (without treatment details) were reviewed by two pediatric experts who provided retrospective antibiotic recommendations. Cases were enrolled upon their consensus. Factors influencing expert-advised antibiotic prescriptions in ED were analyzed.

resultsExpert consensus was achieved in 819/1000 cases. Among these, 174 (21.3%) cases were advised antibiotics by experts (57 cases not prescribed clinically), and 645 (78.7%) cases were not advised for antibiotics (53 cases prescribed clinically). Common diagnoses were upper respiratory tract infections (52.3%), gastroenteritis (18.1%), and lower respiratory tract infections (9.5%) with antibiotic advice rates 24.5%, 14.9%, and 38.5%, respectively. The median age of the antibiotic-advised group was 36 months [interquartile range (IQR): 12, 60], older than the not-advised group[12 months (IQR: 11, 36)]. Median white blood cell count (WBC) and C-reactive protein (CRP) in advised group were 12.9 × 10

conclusionOlder age, fever, and elevated inflammatory markers (WBC, CRP, neutrophil ratio) are key determinants of expert antibiotic recommendations. Inappropriate antibiotic prescriptions highlighted the need for strengthened antibiotic stewardship in pediatric EDs.

Indexed as

Anti-Bacterial AgentsEmergency Service, HospitalGuideline AdherenceInappropriate PrescribingPractice Patterns, Physicians'AdolescentChildChild, PreschoolChinaC-Reactive ProteinCross-Sectional StudiesFemaleHospitals, PediatricHumansInfantMaleAnti-Bacterial AgentsC-Reactive ProteinAntibioticChildrenEmergency departmentPrescription

Identifiers

PMID41814232
PMCPMC13088572

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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.