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Sex estimation is perhaps the most important component of the biological profile. Our sex estimates aid in decedent identification by helping to narrow the missing persons list, and set the basis for many of the remaining biological profile estimates (age, ancestry, stature), which factor in sexual differences. A review of the current research and general literature reveals the use of a wide variety of methodological approaches to sex estimation, which are far from standardized. The goals of this work are (1) reviewing the current state of sex estimation, based on practitioner preferences, case studies, research publications, and proposed standards; (2) presenting and discussing opposing viewpoints regarding issues such as the lack of agreement on terminology standards; whether sex should be estimated at all in subadults; different analytical and data collection approaches; and whether it is appropriate to directly apply sex-specific methods if sex has been positively established, or if keeping a blind approach is still necessary in that case, in order to prevent observer biases; (3) discussing current challenges and future applications; and (4) summarizing the review with a set of recommendations and suggestions for future practice. [ABSTRACT FROM AUTHOR]

The optimized summed scored attributes (OSSA) method was first developed for cranial ancestry estimation (Hefner & Ousley 2014). Tallman and Go (2018) adapted this method for sex estimation with the five skull traits described by Buikstra and Ubelaker (1994) and Walker (2008). Using an Asian sample, Tallman and Go (2018) achieved moderate accuracy rates (83.7% calibration; 81.9% validation) but also high sex bias (29.1% calibration; 34.5% validation), possibly due to lower levels of sexual dimorphism in Asian populations. To further explore this novel approach to sex estimation, the OSSA method was applied to a U.S. Black/African ancestry and White/European ancestry calibration sample (N = 700). Accuracy rates were 77.4% in Black individuals and 77.2% in White individuals. Despite generally higher levels of sexual dimorphism in these groups, a high sex bias still occurred (15.4% Black individuals; -20.5% White individuals) using OSSA. The method was tested in a separate validation sample (N = 200) with accuracy of 78.0% in Black individuals (8.0% sex bias) and 70.0% in White individuals (-56.0% sex bias). When these same traits were tested with Walker's (2008) logistic regression and in the MorphoPASSE Program (Klales 2018) using random forest modeling, accuracy rates varied, with OSSA (77.3% correct), performing slightly better than Walker's (2008) method (75.6% correct) but worse than MorphoPASSE (85.3% correct). The higher accuracy and lower sex bias in MorphoPASSE suggests that the Walker (2008) traits can be used to accurately estimate sex with statistical approaches more appropriate and robust than OSSA. [ABSTRACT FROM AUTHOR]

Morphological skull and pubic traits are routinely used in forensic anthropological casework for sex estimation, with the Walker (2008) and Klales et al. (2012) methods commonly employed. This research aims to present a comprehensive evaluation of reliability in skull and pubic trait scoring and the implications of observer scoring variation on sex estimation accuracy. Results from previous studies are summarized and compiled into tables for comparisons. Data from this study are composed of a large compilation of Walker (2008) and Kales et al. (2012) trait scores (skull n = 392, pubic n = 443) contributed by seven researchers of varying levels of expertise. Intra- and interobserver analyses were performed on the trait scores, and variations in correct sex classifications were assessed among observers, with particular emphasis on the effects of observer experience. Statistical results indicate that the traits utilized in Walker (2008) and Klales et al. (2012) can be reliably scored except for the mental eminence, which has shown considerable variation among studies and individuals. Resultant sex estimations improved with experience level, with the highest accuracy rates for both methods among experts. Although novice observers had good agreement in trait scores with more experienced observers, minor scoring differences negatively impacted classification accuracy, particularly in the Klales et al. (2012) method with more than a 15% drop in accuracy. The results highlight the importance of experience and exposure to human variation and training in these sex estimation methods and also suggests data from expert practitioners can be combined into larger databases. [ABSTRACT FROM AUTHOR]