Testing of morphological sex estimation traits with a sex-known collection: Ottoman period skulls


Yasar B., SAĞIR M.

INTERNATIONAL JOURNAL OF OSTEOARCHAEOLOGY, cilt.33, ss.1042-1051, 2023 (AHCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 33
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1002/oa.3265
  • Dergi Adı: INTERNATIONAL JOURNAL OF OSTEOARCHAEOLOGY
  • Derginin Tarandığı İndeksler: Arts and Humanities Citation Index (AHCI), Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, Periodicals Index Online, Anthropological Literature
  • Sayfa Sayıları: ss.1042-1051
  • Ankara Üniversitesi Adresli: Evet

Özet

Sexual dimorphism patterns vary across geographic regions due to the influence of genetic characteristics and environmental factors. Therefore, sex estimation models are being developed specifically for each population or group. The applicability of morphological sex estimation methods has not been tested in Turkey. Hence, by using skulls, the present study aims to analyze the reliability of the visual morphological method and test the equations developed in different populations. The study material consists of 192 skulls (96 male, 96 female) with known sexes, excavated from Istanbul's Karacaahmet cemetery in 1925. In the present study, glabella, mastoid process, supraorbital margin, and nuchal crest traits were scored on a scale of 1 to 5 according to the instructions provided in standard protocols. Intra-observer and inter-observer agreements were analyzed by two experts having the same level of experience. When equations derived from other populations were applied to our samples, they exhibited high sex biases (up to 50%). Therefore, new equations were derived through binary logistic regression analysis. Glabella had the highest performance in terms of repeatability (0.83) and reproducibility (0.74), whereas the nuchal crest showed the lowest performance (0.60-0.52). The most significant sexual dimorphism was observed in the glabella. Based on cross-validated results using a single criterion, it accurately classified 80% of females and 84% of males. The nuchal crest was not significantly affecting the sex discriminative equations (p > 0.05). Multivariate equations achieved an accuracy of over 90% and cross-validated results ranged between 80% and 90%. The results obtained from present study support the hypothesis that sexual dimorphism patterns vary under different conditions and highlight the importance of population variation in sex estimation. The models derived from the present study were found to be suitable for sex estimation from skulls and demonstrated high performance.