Sexual Dimorphism of Cranial Morphological Traits in an Italian Sample: A Population-Specific Logistic Regression Model for Predicting Sex

Although not without subjectivity, the cranial trait scoring method is an easy visual method routinely used by forensic anthropologists in sex estimation. The revision presented by Walker in 2008 has introduced predictive models with good accuracies in the original populations. However, such models may lead to unsatisfactory performances when applied to populations that are different from the original. Therefore, this study aimed to test the sex predictive equations reported by Walker on a contemporary Italian population (177 individuals) in order to evaluate the reliability of the method and to identify potential sexual dimorphic differences between American and Italian individuals. In order to provide new reference data to be used by forensic experts dealing with human remains of modern/contemporary individuals from this geographical area, we designed logistic regression models specific to our population, whose accuracy was evaluated on a validation sample from the same population. In particular, we fitted logistic regression models for all possible combinations of the five cranial morphological traits (i.e., nuchal crest, mastoid process, orbital margin, glabella, and mental eminence). This approach provided a comprehensive set of population-specific equations that can be used in forensic contexts where crania might be retrieved with severe taphonomic damages, thus limiting the application of the method only to a few morphological features. The results proved once again that the effects of secular changes and biogeographic ancestry on sexual dimorphism of cranial morphological traits are remarkable, as highlighted by the low accuracy (from 56% to 78%) of the six Walker's equations when applied to our female sample. Among our fitted models, the one including the glabella and mastoid process was the most accurate since these features are more sexually dimorphic in our population. Finally, our models proved to have high predictive performances in both training and validation samples, with accuracy percentages up to 91.7% for Italian females, which represents a significant success in minimizing the potential misclassifications in real forensic scenarios.