Literature DB >> 26048508

The challenges of accurately estimating time of long bone injury in children.

Tracy A Pickett1.   

Abstract

The ability to determine the time an injury occurred can be of crucial significance in forensic medicine and holds special relevance to the investigation of child abuse. However, dating paediatric long bone injury, including fractures, is nuanced by complexities specific to the paediatric population. These challenges include the ability to identify bone injury in a growing or only partially-calcified skeleton, different injury patterns seen within the spectrum of the paediatric population, the effects of bone growth on healing as a separate entity from injury, differential healing rates seen at different ages, and the relative scarcity of information regarding healing rates in children, especially the very young. The challenges posed by these factors are compounded by a lack of consistency in defining and categorizing healing parameters. This paper sets out the primary limitations of existing knowledge regarding estimating timing of paediatric bone injury. Consideration and understanding of the multitude of factors affecting bone injury and healing in children will assist those providing opinion in the medical-legal forum.
Copyright © 2015 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Entities:  

Keywords:  Child abuse; Dating; Fractures; Paediatrics

Mesh:

Year:  2015        PMID: 26048508     DOI: 10.1016/j.jflm.2015.04.012

Source DB:  PubMed          Journal:  J Forensic Leg Med        ISSN: 1752-928X            Impact factor:   1.614


  2 in total

1.  Association of Parental Mental Illness With Child Injury Occurrence, Hospitalization, and Death During Early Childhood.

Authors:  Shiow-Wen Yang; Mary A Kernic; Beth A Mueller; Gregory E Simon; Kwun Chuen Gary Chan; Ann Vander Stoep
Journal:  JAMA Pediatr       Date:  2020-08-03       Impact factor: 16.193

2.  Postmortem and Antemortem Forensic Assessment of Pediatric Fracture Healing from Radiographs and Machine Learning Classification.

Authors:  Kelsey M Kyllonen; Keith L Monson; Michael A Smith
Journal:  Biology (Basel)       Date:  2022-05-13
  2 in total

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