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利用南非温带地区的积日法(ADD)估算死后经过时间(PMI)。

Estimating the postmortem interval (PMI) using accumulated degree-days (ADD) in a temperate region of South Africa.

机构信息

Forensic Anthropology Research Centre, Department of Anatomy, School of Medicine, Faculty of Health Sciences, University of Pretoria, South Africa.

出版信息

Forensic Sci Int. 2013 Jun 10;229(1-3):165.e1-6. doi: 10.1016/j.forsciint.2013.03.037. Epub 2013 Apr 16.

DOI:10.1016/j.forsciint.2013.03.037
PMID:23601149
Abstract

The validity of the method in which total body score (TBS) and accumulated degree-days (ADD) are used to estimate the postmortem interval (PMI) is examined. TBS and ADD were recorded for 232 days in northern South Africa, which has temperatures between 17 and 28 °C in summer and 6 and 20 °C in winter. Winter temperatures rarely go below 0°C. Thirty pig carcasses, which weighed between 38 and 91 kg, were used. TBS was scored using the modified method of Megyesi et al. [1]. Temperature was acquired from an on site data logger and the weather station bureau; differences between these two sources were not statistically significant. Using loglinear random-effects maximum likelihood regression, an r(2) value for ADD (0.6227) was produced and linear regression formulae to estimate PMI from ADD with a 95% prediction interval were developed. The data of 16 additional pigs that were placed a year later were then used to validate the accuracy of this method. The actual PMI and ADD were compared to the estimated PMI and ADD produced by the developed formulae as well as the estimated PMIs within the 95% prediction interval. A validation of the study produced poor results as only one pig of 16 fell within the 95% interval when using the formulae, showing that ADD has limited use in the prediction of PMI in a South African setting.

摘要

研究了使用全身评分(TBS)和累积度日数(ADD)来估计死后间隔时间(PMI)的方法的有效性。在南非北部记录了 232 天的 TBS 和 ADD,那里夏季的温度在 17 到 28°C 之间,冬季的温度在 6 到 20°C 之间。冬季温度很少降至 0°C 以下。使用了 30 具体重在 38 到 91 公斤之间的猪尸。TBS 采用 Megyesi 等人的改良方法进行评分[1]。温度由现场数据记录器和气象局获得;这两个来源之间的差异没有统计学意义。使用对数线性随机效应最大似然回归,生成了 ADD 的 r(2)值(0.6227),并开发了从 ADD 估计 PMI 的线性回归公式,并具有 95%的预测区间。然后使用 16 头一年后放置的额外猪的数据来验证该方法的准确性。实际的 PMI 和 ADD 与开发公式产生的估计 PMI 和 ADD 以及 95%预测区间内的估计 PMI 进行了比较。该研究的验证结果不佳,因为只有 16 头猪中的一头使用公式落在 95%区间内,表明 ADD 在南非环境中预测 PMI 的应用有限。

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