技术说明:根据身材和骨盆宽度预测体重。

IF 1.7 2区 生物学 Q1 ANTHROPOLOGY
Christopher B. Ruff, Ian J. Wallace, Amaya Abeyta-Brown, Madison Butler, Taylor Busby
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引用次数: 0

摘要

根据身材和双髂(骨盆最大宽度)宽度预测体重的方程已经开发出来,但在应用于在世或刚去世的个体时,其成功率参差不齐,因此其普遍适用性受到质疑。在这里,我们在一个大型的、种族多样化的样本中对这些公式进行了测试。我们从新墨西哥州死者图像数据库中获得了 507 位近期去世的土著、西班牙裔和非西班牙裔白人成年人的骨骼和人体测量数据。体重指数 "正常"(BMI = 18.5-24.9)者的体重预测非常准确,平均方向偏差约为 1%,平均随机误差小于 8%。体重不足者(BMI
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Technical note: Prediction of body mass from stature and pelvic breadth

Technical note: Prediction of body mass from stature and pelvic breadth

Technical note: Prediction of body mass from stature and pelvic breadth

Equations for predicting body mass from stature and bi-iliac (maximum pelvic) breadth have been developed, but have had variable success when applied to living or recently deceased individuals, calling into question their general applicability. Here we test these equations on a large, ethnically diverse sample. Skeletal and anthropometric data for 507 recently deceased Indigenous, Hispanic, and non-Hispanic White adults were obtained from the New Mexico Decedent Image Database. The body mass of individuals with a “normal” body mass index (BMI = 18.5–24.9) is very accurately predicted, with an average directional bias of about 1% and an average random error of less than 8%. Underweight individuals (BMI < 18.5) are overpredicted, while overweight (BMI = 25–29.9) and especially obese (BMI≥30) individuals are underpredicted. Within BMI categories, there is a strong and isometric relationship between predicted and true body mass. Individual body mass prediction errors using the stature/bi-iliac method are mainly dependent on variation in BMI. Because earlier humans were more likely to fall within or close to the normal BMI range, the equations should be applicable, on an individual basis, in archeological and paleontological contexts. Because of the prevalence of obesity in many modern populations, these equations are not applicable in a general forensic context. We derive new equations from nonobese individuals in our sample (n = 338), which produce reasonable average prediction errors. If obese individuals can be identified using other skeletal parameters, these equations may be useful in estimating body mass in nonobese forensic cases.

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