Literature DB >> 32823079

Technical note: Development of regression equations to reassociate upper limb bones from commingled contexts.

Ioanna Anastopoulou1, Fotios Alexandros Karakostis2, Constantine Eliopoulos3, Konstantinos Moraitis4.   

Abstract

The major upper limb skeletal elements (scapulae, humeri, ulnae and radii) are frequently utilized for sex determination and stature estimation. Consequently, in forensic cases that involve commingled remains, it is crucial to reassociate the aforementioned bones and attribute them to the right individual. The aim of the present study is to develop simple and multiple regression equations for sorting commingled human skeletal elements of the upper limb. In that context, ten common anthropological linear measurements of the articular surfaces of scapulae, humeri, ulnae, and radii were performed on 222 adult skeletons from the Athens Collection. The functions developed for sorting adjoining bones presented a strong positive linear relationship (r=0.69-0.93, p<0.05). The values of the determination coefficient statistics (r2=0.47-0.86) were found to be high and those of the standard errors of the estimate were found to be low (SEE=0.88-1.61). Blind tests indicated that when metric and morphoscopic sorting techniques are combined, a reliable sorting of the skeletal elements of the upper limbs is possible.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Commingling; Forensic anthropology; Osteometric sorting; Reassociation; Regression analysis; Upper limb bones

Mesh:

Year:  2020        PMID: 32823079     DOI: 10.1016/j.forsciint.2020.110439

Source DB:  PubMed          Journal:  Forensic Sci Int        ISSN: 0379-0738            Impact factor:   2.395


  1 in total

1.  Accurate and semi-automated reassociation of intermixed human skeletal remains recovered from bioarchaeological and forensic contexts.

Authors:  Ioanna Anastopoulou; Fotios Alexandros Karakostis; Katerina Harvati; Konstantinos Moraitis
Journal:  Sci Rep       Date:  2021-10-12       Impact factor: 4.379

  1 in total

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