AI Article Synopsis

  • Establishing the identity of deceased individuals from mass fatalities is crucial, especially for highly decomposed, mutilated, or skeletal remains, necessitating the estimation of stature, age, sex, and race/ethnicity.
  • Studies on unidentified remains in Nepal, especially following the Maoist insurgency, face challenges due to the lack of anthropological data on the local population.
  • This research analyzed 200 autopsied cases, measuring cranial dimensions and finding that multiple regression models provide a more accurate estimation of stature from cranial measurements compared to simpler models, presenting valuable baseline data for forensic applications.

Article Abstract

Establishing the identity of the deceased becomes essential when highly decomposed bodies, mutilated body parts or skeletal remains are recovered from mass fatality sites. In these situations, estimation of stature along with other parameters such as age, sex and race/ethnicity becomes important to establish the biological profile of the deceased. Following the Maoist insurgency in Nepal, there have been numerous discoveries of unidentified human remains in mass graves or otherwise. No systemic studies and anthropological data on the Nepalese population however, is available posing problems in anthropologic evaluation of the remains. The sample of the present study consisted of 200 autopsied cases (148 males and 52 female adult cadavers). During the autopsy, the scalp was reflected after giving a coronal incision extending from one mastoid to the other exposing the cranium in each case. Maximum cranial length (MCL), maximum cranial breadth (MCB), bi-zygomatic breadth (BZB), minimum frontal breadth (MFB) and length of parietal chord (PC) were then measured. Stature was measured as the length of the body from head to heel in centimeters with the heel, buttocks, back of the shoulders and the head in contact with the autopsy table. Linear and stepwise multiple regression models were derived for estimation of stature from cranial measurements. Univariate, bivariate and multivariate regression models show statistically significant correlation between stature and the cranial measurements. The present study opines that the stature estimation from cranial dimensions using multivariate linear regression models is more accurate than those of the univariate and bivariate regression models. This study presents a rare data from Nepalese population that show typical Asian features and thus, is significant from anthropologic and genetic point of view. The study observations further contribute a baseline data bank for forensic pathologists and specialists.

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Source
http://dx.doi.org/10.1016/j.forsciint.2014.12.014DOI Listing

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