Estimation of sex based on metrics of the sternum in a contemporary Jordanian population: A computed tomographic study

There is a paucity of osteometric standards for sex estimation from unknown skeletal remains in Jordan and the sexual dimorphism of the sternum has not yet been investigated. The aim of this study was to evaluate the sexual dimorphism in sternal measurements using 3D multidetector computed tomography (MDCT), and to assess their reliability for sex estimation in a Jordanian population. A total of 600 MDCT scans (300 males and 300 females) were used and a total of 8 sternal measurements were studied (manubrium length, sternal body length, combined length of manubrium and body, corpus sterni width at 1st and 3rd sternebrae, sternal index and area). Sexual dimorphism was evaluated by means of discriminant function analyses. Significant sexual dimorphism was found mainly in middle-aged and older adults. Including all subjects, multivariate, and stepwise functions gave an overall accuracy of 83.0% and 84.0%, respectively. Additionally, multivariate and stepwise analyses were conducted separately for each age group. The accuracy of sex estimation in multivariate analysis (all variables) varied from 63.2% in the young, and 83.7% in the middle adults to 84.9% for older adults. In stepwise analysis, the highest accuracy rates were provided by only sternal area in young adults (81.6%), and sternal area combined with sternal body length in middle-aged and older adults (84.2% and 85.3%, respectively). The best sex discriminator using univariate analysis (single variable) was sternal area followed by sternal body length (84.0% and 80.8% respectively). Notably, univariate analyses for most variables gave relatively higher classification accuracies in females but were poor at predicting males in the sample (sex bias ranged between -6.4% and -20%). Our data suggest that dimorphism in the human sternum increases with advancing age and separate discriminant functions are needed for each age group in Jordanians. In addition, multivariate and stepwise analyses using sternum gave higher classification accuracies with comparatively lower sex biases compared to univariate analyses.