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  • Title: Estimating sex of the human skeleton based on metrics of the sternum.
    Author: Bongiovanni R, Spradley MK.
    Journal: Forensic Sci Int; 2012 Jun 10; 219(1-3):290.e1-7. PubMed ID: 22209293.
    Abstract:
    Estimating the sex of skeletal remains is critical in creating the biological profile of an adult individual. Previous research has shown that analyzing the sternum may lead to an accurate estimation of sex based on studies performed on various populations around the globe, such as Indian, European, African, Canadian and North American. The motivation of the current study is to develop classification functions and sectioning points for use in forensic investigations in the United States. The majority of previously published methods are population specific, meaning the data would not prove useful in the United States. For this study, sternal measurements were collected from the William M. Bass Donated Skeletal Collection located at The University of Tennessee, Knoxville following the metric definitions provided by Schwartz and Bass. This collection consists of recent skeletal remains with known age at death, ancestry and sex. Material for the present study consisted of 410 human sterna: 285 male (256 American White/29 American Black) and 125 female (120 American White/5 American Black). Previous studies analyze the applicability of Hyrtl's law in the estimation of sex. This law follows that the mesosternum is greater than twice the length of the manubrium in males and in females the length of the manubrium is greater than half the length of the mesosternum. In this study, comparisons of the proportion of the length of the manubrium to the length of the mesosternum were performed to determine if Hrytl's law is applicable in an American population. Comparisons of these measurements between individuals identified as American Black and American White were analyzed to determine whether this method could be used on both population groups. Further, discriminant function analysis was used to estimate sex and provide a population specific classification function for use in the United States. The discriminate function analysis produced an overall cross-validation classification rate of 84.12% for sex estimation. The cross-validation classification rate for males and females was 80.00% and 88.24%, respectively.
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