An investigation of time since injury: a radiographic study of fracture healing

Working at the junction of medicine and physical anthropology, this research investigates the rate of fracture healing. The ability to assign ages to fractures based on the degree of remodeling could be a valuable tool for identifying skeletal remains. This ability could differentiate between individuals with similar fractures and could also narrow the search of medical records for matches. Multiple radiographic images from 62 individuals were collected from the Baton Rouge Orthopaedic Clinic, including information on sex, ancestry, age of the individual, and age of the fracture. Breaks in the x-rays are categorized into one of six stages, defined on the basis of observable characteristics in radiographs. Variables of age, sex, ancestry, and type of fixation (ie, internal or external) are tested against the stage of the fracture and the time since the initial diagnosis of the fracture. Univariate analysis of variance shows that age is the only variable investigated in this study that repeatedly shows a significant correlation to the age of a particular fracture. Further research is needed to draw concrete conclusions and develop acceptable ranges for dating fractures

Age Estimation with Decision Trees: Testing the Relevance of 94 Aging Indicators on the William M. Bass Donated Collection

Anthropologists have been estimating ages-at-death of skeletons for a long time. A variety of different age indicators has been studied and age estimation methods have been developed in an attempt to standardize the process. Even with all the work that has gone into developing age estimation methods, age estimation of mature skeletons is still very imprecise. This research investigates various age indicator definitions and their performance on an elderly skeletal sample. Using 176 individuals from the William M. Bass Donated Collection curated in the Department of Anthropology at the University of Tennessee, Knoxville, data were collected on age indicators gathered from fifteen age estimation methods. Ninety-four variables were tested with various decision trees to show patterns among the variables. Regression equations were built using the same variables as the decision trees, and the performance between the two methodologies were compared. The decision trees performed slightly better, with a mean absolute error of prediction of around five years. Variable occurrence was tabulated across various decision tree models. The most common variables are pit shape of the sternal rib end morphology and the phase of the auricular phase. These two variables, along with others commonly selected, present best candidates for building an age estimation method that pertains to older populations