...
首页> 外文期刊>Forensic science international >Facial soft tissue thicknesses in craniofacial identification: Data collection protocols and associated measurement errors
【24h】

Facial soft tissue thicknesses in craniofacial identification: Data collection protocols and associated measurement errors

机译:颅面识别中的面部软组织厚度:数据收集协议和相关测量误差

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       

摘要

Facial soft tissue thicknesses (FSTT) form a key component of craniofacial identification methods, but as for any data, embedded measurement errors are highly pertinent. These in part dictate the effective resolution of the measurements. As herein reviewed, measurement methods are highly varied in FSTT studies and associated measurement errors have generally not been paid much attention. Less than half (44%) of 95 FSTT studies comment on measurement error and not all of these provide specific quantification. Where informative error measurement protocols are employed (5% of studies), the mean error magnitudes range from 3% to 45% rTEM and are typically in the order of 10-20%. These values demonstrate that FSTT measurement errors are similar in size to (and likely larger than) the magnitudes of many biological effects being chased. As a result, the attribution of small millimeter or submillimeter differences in FSTT to biological variables must be undertaken with caution, especially where they have not been repeated across different studies/samples. To improve the integrity of FSTT studies and the reporting of FSTT measurement errors, we propose the following standard: (1) calculate the technical error of measurement (TEM or rTEM) in any FSTT research work; (2) assess the error embedded in the full data collection procedure; and (3) conduct validation testing of FSTT means proposed for point estimation prior to publication to ensure newly calculated FSTT means provide improvements. In order to facilitate the latter, a freely available R tool TDValidator that uses the C-Table data for validation testing is provided. (C) 2019 Elsevier B.V. All rights reserved.
机译:面部软组织厚度(FSTT)形成颅面识别方法的关键组分,但对于任何数据,嵌入式测量误差是高度相关的。这些部分决定了测量的有效分辨率。如本文所述,在FSTT研究中,测量方法在FSTT研究中具有高度多样化,并且相关的测量误差通常不受很多关注。不到一半的95 FSTT研究关于测量误差的评论,而不是所有这些都提供了具体的量化。其中采用信息误差测量协议(5%的研究),平均误差幅度范围为3%至45%的RTEM,通常为10-20%。这些值表明FSTT测量误差的尺寸与(并且可能大于)追加许多生物学效应的大小。结果,必须谨慎地进行FSTT到生物变量的小毫米或淹没率差的归因,特别是在不同的研究/样品中未重复它们。为改善FSTT研究的完整性和FSTT测量误差的报告,我们提出以下标准:(1)计算任何FSTT研究工作中的测量(TEM或RTEM)的技术误差; (2)评估完整数据收集程序中嵌入的误差; (3)进行FSTT手段的验证测试,提出在发布之前进行点估计,以确保新计算的FSTT手段提供改进。为了便于后者,提供了一种可自由的R工具TDValidator,用于使用C表数据进行验证测试。 (c)2019年Elsevier B.V.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号