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一种使用髋骨和股骨进行性别判定的度量方法。

A metric method for sex determination using the hipbone and the femur.

作者信息

Albanese John

机构信息

Department of Anthropology, McMaster University, Hamilton, Ontario, L8S 4L9, Canada.

出版信息

J Forensic Sci. 2003 Mar;48(2):263-73.

Abstract

Since the earliest descriptions of the pubis length measurement, it has been recognized that the location of the key landmark in the acetabulum has to be estimated. Using samples from the Terry Collection (n = 324) and the Coimbra Collection (n = 232), the purpose of this research is to, first, test the reproducibility of a new alternative to the traditional measurement of the pubis, and second, to use the best measurement of the pubis along with other measurements of the hipbone and femur to develop a metric method that can be used with confidence to determine the sex of individuals of various geographic origins and time periods. In this study, it was found that, first, the alternative pubis measurement, known as the superior pubis ramus length (SPRL), can be collected more reliably with less mean intra-observer error (0.57%) than the more commonly used manner of measuring the pubis (2.7%). Second, a logistic regression sex determination method using the SPRL, along with other measurements of the hipbone and femur, has an allocation accuracy of 90% to 98.5% (depending on the model used and the manner of testing) across independent samples. Third, traditional racial categorization was irrelevant to the accuracy of the method. Fourth, measurement error greater than 2% in the measurement of the pubis can be the difference between a correct and an incorrect allocation of sex, particularly in borderline cases.

摘要

自耻骨长度测量首次被描述以来,人们就已经认识到髋臼中关键标志的位置必须进行估计。本研究以特里收藏(n = 324)和科英布拉收藏(n = 232)的样本为基础,目的一是测试一种新型耻骨测量方法替代传统测量方法的可重复性,二是结合耻骨的最佳测量值以及髋骨和股骨的其他测量值,开发一种可放心用于确定不同地理起源和时间段个体性别的计量方法。在本研究中,发现:其一,被称为耻骨上支长度(SPRL)的新型耻骨测量方法,与更常用的耻骨测量方式相比,收集时更可靠,平均观察者内误差更小(0.57%对2.7%)。其二,使用SPRL以及髋骨和股骨的其他测量值的逻辑回归性别判定方法,在独立样本中的分配准确率为90%至98.5%(取决于所使用的模型和测试方式)。其三,传统的种族分类与该方法的准确性无关。其四,耻骨测量误差大于2%可能导致性别分配正确与否的差异,尤其是在临界情况下。

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