Subadult sex estimation from diaphyseal dimensions

Bibliographic Data

ID8313634
AuthorsK E Stull (0000-0002-4541-6777, Department of Anthropology University of Nevada Reno, Reno NV, corresponding author), Ericka N L’abbé (0000-0002-6722-8814, University of Pretoria), Ericka N L'Abbé (Department of Anatomy Faculty of Health Sciences, University of Pretoria South Africa), Stephen D Ousley (0000-0001-8210-2665, Department of Anthropology Mercyhurst University Erie PA)
Year2017
Volume163
Issue1
Pages64-74
Publication date2017-05-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueAmerican Journal of Physical Anthropology (JOURNAL)
Journal identifiersISSN: 0002-9483 • E-ISSN: 1096-8644
PublisherWiley (PUBLISHER • GB)
DOI10.1002/ajpa.23185
PMID28198540
OpenAlexW2588819596
LanguageEN
Citations received11
References cited65

OBJECTIVES: Many studies on subadult sex estimation focus on elements that express sexually dimorphic features in adults. In contrast, diaphyseal dimensions have been shown to display sex-specific differences prior to adolescence. The current study evaluates the use of diaphyseal dimensions in subadult sex estimation. MATERIALS AND METHODS: Eighteen postcranial measurements from six long bones were collected on Lodox Statscan radiographic images of 1,310 modern South African children between birth and 12 years of age. Linear (LDA) and flexible discriminant analysis (FDA) and logistic regression were employed with single and multiple variable models with age both included and excluded from the model. Bootstrapped cross-validation was employed because some of the multiple variable subsets had small sample sizes. Each of the bootstrapped accuracies has an associated 95% confidence interval demonstrating the ranges in classification. RESULTS: Classification methods utilizing multiple variables achieved the highest bootstrapped classification accuracies (70% to 93%). The inclusion of age in the models did not consistently increase or decrease the classification accuracies. Proximal and distal breadth measurements were consistently recognized as important measurements in model creation. FDA yielded the highest overall accuracies, but the logistic regression presented with overall smaller bootstrapped 95% confidence intervals. DISCUSSION: Quantifiable sex differences were discovered in the appendicular skeleton of children between birth and 12 years of age. The high classification accuracies were likely due to using numerous predictor variables from multiple skeletal elements, which were optimized for classification using FDA. To facilitate application, a graphical user interface, KidStats, was developed

Confidence interval · Discriminant · Estimation · Linear discriminant analysis · Linear regression · Logistic regression · Regression · Regression analysis · Statistics · Artificial Intelligence · Computer Science · Demography · Dental Radiography and Imaging · Forensic Anthropology and Bioarchaeology Studies · Mathematics · Orthodontics and Dentofacial Orthopedics

Tools

Open DOISci-Hub