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A Model to Improve Detection of Nonaccidental Pediatric Burns

Overview of attention for article published in The AMA Journal of Ethic, June 2018
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Title
A Model to Improve Detection of Nonaccidental Pediatric Burns
Published in
The AMA Journal of Ethic, June 2018
DOI 10.1001/journalofethics.2018.20.6.org1-1806
Pubmed ID
Authors

Lauren C Nigro, Michael J Feldman, Robin L Foster, Andrea L Pozez

Abstract

Pediatric burn patients warrant thorough evaluation because a sizeable proportion of pediatric burns are nonaccidental. A multidisciplinary method involving an internal child protection team (CPT) was developed and used to identify suspected nonaccidental pediatric burns in all pediatric burn patients 5 years of age or younger who were evaluated by the CPT and social workers at our institution over a 55-month period. We identified 343 cases for review that fit our age criteria, 6 of which we identified as cases of suspected abuse or neglect. On average, these patients were younger, suffered greater total body surface area burns (TBSA), and required a longer length of stay in the hospital than the total population. We have not had readmissions for repeat nonaccidental pediatric burn injuries in this group of patients since this model was implemented. Our multidisciplinary method might provide a more consistent and reliable method for identifying cases of suspected abuse.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 17%
Student > Bachelor 4 17%
Other 3 13%
Student > Master 3 13%
Student > Doctoral Student 2 8%
Other 3 13%
Unknown 5 21%
Readers by discipline Count As %
Medicine and Dentistry 10 42%
Nursing and Health Professions 4 17%
Psychology 1 4%
Biochemistry, Genetics and Molecular Biology 1 4%
Social Sciences 1 4%
Other 1 4%
Unknown 6 25%