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Rib Fractures: Validation of an Interdisciplinary Classification System

Angela L Harden, Yun-Seok Kang, Amanda M. Agnew

Abstract


Thoracic injuries, specifically rib fractures, are of significant clinical and forensic interest and occur in high frequencies in homicides, accidents, and motor vehicle crashes. However, current analysis of rib fractures lacks a systematic method for characterizing and interpreting these injuries. The goal of the present research is to provide a standardized classification system dedicated to describing and quantifying fractures for utilization across scientific disciplines. The novel system was designed around identifying rib fracture characteristics (location, pattern of propagation, number, and severity) and using these characteristics to describe, quantify, and classify fractures using a three-part hierarchical method. For validation, mid-thoracic ribs (n = 347) were subjected to dynamic anterior-posterior blunt force loading (i.e., a bending mechanism), and the resulting fractures were analyzed using our rib fracture classification system. Fracture locations, defined as a percentage of the total curve length of each rib, ranged from 11–89% (mean = 63%), and six different fracture groups were observed. The number of fractures observed per rib ranged from one to three, and injury severities—based on the hierarchical severity
of each fracture group and the number of fractures present—of each rib ranged from minor to severe. The success rate in classifying fractures was 99.2%. This novel fracture classification system provides additional detail and clarity for defining features of rib fractures compared to previous methods. By establishing common language and specific fracture characteristics to include in all rib fracture analyses,
this classification system has the potential to be utilized across disciplines (i.e., clinical, forensic, and biomechanical settings).


Keywords


forensic anthropology, skeletal trauma, trauma analysis, forensic methods, injury biomechanics, fracture pattern

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DOI: http://dx.doi.org/10.5744/fa.2019.1032