A new method for the interpretation of veterinary forensic necropsy and PMCT finding with Bayesian decision theory

Mara Rita Rodrigues Massad , Hélio Junji Shimozako , Laila Massad Ribas , Ana Carolina Brandão de Campos Fonseca Pinto , Hock Gan Heng , Tália Missen Tremori , Sérvio Túlio Jacinto Reis , Eduardo Massad , Noeme Sousa Rocha
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引用次数: 1

Abstract

This work proposes a novel approach to estimate the sensitivity of post-mortem computed tomography (PMCT) with respect to traditional necropsy (gold standard). Using concepts of Bayes theorem and the opinion of 57 veterinarians of how a relevant finding (RF) on PMCT or at necropsy can be a determinant in the animal's death, the general sensitivity was estimated. Such sensitivity can be interpreted as the probability to identify a specific RF in a necropsied animal. In the study, we included 18 animals that were subjected to traditional necropsy and underwent PMCT, which provided 48 different findings; all were assumed as a potential cause of death. This study aimed to understand the probability of death, given that a necropsied animal presented a specific RF. To estimate all contributions of the 48 RF, we designed a simple survey based on the Delphi method. In this survey, we invited veterinarians to evaluate the 48 NF and to assign a grade in a Likert scale from 0 to 10 for each of the RFs. In this scale, 0 (zero) indicates that the NF does not contribute to the animal's death, and 10 (ten) indicates that this NFdoes totally contributes to the animal's death. Therefore, each veterinarian, according to their own professional experience, assigns a grade describing how important each necroscopic finding is to the animal's death. Our results demonstrated that the problem of sensitivity analysis in the absence of a gold standard could be circumvented by the method proposed here.

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用贝叶斯决策理论解释兽医法医尸检和PMCT发现的新方法
这项工作提出了一种新的方法来估计死后计算机断层扫描(PMCT)相对于传统尸检(金标准)的敏感性。利用贝叶斯定理的概念和57名兽医关于PMCT或尸检的相关发现(RF)如何成为动物死亡的决定因素的意见,估计了一般敏感性。这种敏感性可以解释为在尸检动物中识别特定RF的概率。在这项研究中,我们纳入了18只接受传统尸检并进行PMCT的动物,提供了48种不同的发现;所有这些都被认为是潜在的死因。本研究的目的是了解死亡的概率,假设死后的动物呈现特定的RF。为了估计48个RF的所有贡献,我们设计了一个基于德尔菲法的简单调查。在这项调查中,我们邀请兽医对48个NF进行评估,并在李克特量表中为每个rf分配从0到10的等级。在这个量表中,0(零)表示NF不导致动物死亡,10(十)表示NF完全导致动物死亡。因此,每个兽医,根据他们自己的专业经验,分配一个等级,描述每一个坏死性检查结果对动物死亡的重要程度。我们的结果表明,在没有金标准的情况下,灵敏度分析的问题可以通过本文提出的方法来解决。
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来源期刊
Journal of Forensic Radiology and Imaging
Journal of Forensic Radiology and Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
1.70
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