Utilize este identificador para referenciar este registo: http://hdl.handle.net/10362/101234
Título: Sex prediction based on mesiodistalwidth data in the Portuguese population
Autor: Neves, João Albernaz
Antunes-Ferreira, Nathalie
Machado, Vanessa
Botelho, João
Proença, Luís
Quintas, Alexandre
Mendes, José João
Delgado, Sintra
Palavras-chave: Dental measurements
Forensic dentistry
Portuguese population
Predictive model
Sex determination
Sexual dimorphism
Applied Mathematics
Data: 1-Jun-2020
Resumo: Accurate sex prediction is a key step in creating a postmortem forensic profile as it excludes approximately half the population. It is our goal to develop a predictive model to establish sex through teeth mesiodistal widths in a Portuguese population. The pretreatment dental casts of 168 of Portuguese orthodontics subjects (59 males and 109 females) were included. Mesiodistal widths from right first molar to left first molar were measured on each pretreatment cast to the nearest 0.01 mm using a digital caliper. Overall, the mesiodistal widths of the upper and lower canines, premolars, and molars were found to be significantly different between females and males. Conversely, no significant differences between sexes were identified for incisors. A multivariate logistic regression model for sex prediction was developed and the teeth included in the final reduced model being the upper left canine (2.3), the lower right lateral incisor (4.2) and the lower right canine (4.3). There is a prevalence of sexual dimorphism in all teeth except the incisors. The canines present the most noticeable difference between sexes. The presented sex determination predictive model exhibits an overall correct classification of 75%, outperforming all available models for this purpose and therefore is a potential tool for forensic analysis in this population.
Descrição: UIDB/04038/2020 UIDP/04038/2020
Peer review: yes
URI: http://hdl.handle.net/10362/101234
DOI: https://doi.org/10.3390/app10124156
ISSN: 2076-3417
Aparece nas colecções:FCSH: CRIA - Artigos em revista internacional com arbitragem científica

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