Estimating Stature and Gender by the Length of the Third, Fourth, and Fifth Fingers of Dominant Hand in Iranian Adults
Background: Stature and gender are essential parameters of forensic anthropology. Moreover, their estimation is critical for medico-legal investigations to identify unknown remains. The present study aimed to estimate stature and gender by the anthropometric dimensions of the Iranian population’s th...
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Published in: | International journal of medical toxicology and forensic medicine Vol. 12; no. 1; p. 34718 |
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Main Authors: | , , , , |
Format: | Journal Article |
Language: | English |
Published: |
Shaheed Beheshti University of Medical Sciences
13-03-2022
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Subjects: | |
Online Access: | Get full text |
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Summary: | Background: Stature and gender are essential parameters of forensic anthropology. Moreover, their estimation is critical for medico-legal investigations to identify unknown remains. The present study aimed to estimate stature and gender by the anthropometric dimensions of the Iranian population’s third, fourth, and fifth fingers. Methods: In this cross-sectional study, 199 individuals (99 men & 100 women) were included. Individuals’ demographic information was recorded. A caliper measured the length of the fingers. An anthropometer was used for the measurement of stature. The obtained data were analyzed using SPSS. A multivariate linear regression test was used to predict the stature of individuals based on finger length. Results: In males and females, a solid and positive direct relationship was observed between the length of the third, fourth, and fifth fingers of the hand with the stature of individuals and between the length of the fingers with each other. In the coefficient table of the regression model of this study, in males, the length of the fingers was not predictive of stature, and only the length of the third finger in females had a predictive effect on stature (P=0.007). Conclusion: According to the obtained results, stature is predictable in women through the third finger length using the derived regression equation. |
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ISSN: | 2251-8762 2251-8770 |
DOI: | 10.32598/ijmtfm.v12i1.34718 |