Using shape analysis and human variation to better predict sex in the human coxal bone (2020)
Metric methods of sex estimation are often less powerful than visual methods because linear measurements represent too may isometric measures of body size and lack sufficient allometric measures of body form (size and shape). This study uses geometric morphometrics to identify 17 landmarks that most effectively represent sex-based shape in right and left coxal bones (n = 394, f = 191, m = 203), these are: the anterior superior iliac spine; posterior superior iliac spine; posterior inferior iliac spine; iliac crest; apex of the auricular surface; greater sciatic notch; ischial spine; superior, inferior and distal points on ischial tuberosity; superior, inferior and midpoint on the symphyseal face; arcuate eminence; ischiopubic ramus; and posterosuperior and anterosuperior points on the acetabular rim. The first and second principal components (PCs) correctly predicted sex in 98.5% of cases; better than previous studies on whole coxal bone sex-based shape. Linear measurements from Langley et al. (2016) that correspond with the 17-landmarks were used to generate a reliable discriminant function (DF) equation and logistic regression model (LRM) for sex estimation. The DF equation correctly predicted sex 99.7% of the time in cross-validation, the LRM correctly predicted sex in all individuals. Both equations accounted for allometric size, isometric size, and fluctuating asymmetry to help discern sex from other variants of shape. When tested on an independent population (n = 120; f = 60/60, m = 60/60), the DF equation correctly predicted sex with 99.2% accuracy (f = 191/191, 100%, m = 202/203, 99.7%), and the LRM correctly predicted sex in all test specimens.Measurements and landmarks were further tested for use in fragmented coxal bones. The most successful DFs and LRMs accurately predicted sex between 98.7 – 99.2% for measurements representing coxal bones completeness between 50-25%. DF and LRM equations representing coxal bones no less than 25% complete predicted sex with similar accuracies (DF = 99.0%; LRM = 99.2%) and correctly assigned 100% of the test population. These equations excelled at sex estimation because the measurements account for variations in sex, size (allometry and isometry) and fluctuating asymmetry. These DF and LRM equations are recommended for forensic applications.
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