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兽医学中的临床评分:评分构建、可靠性和验证存在哪些陷阱?一种应用于牛的通用方法学途径

Clinical Scores in Veterinary Medicine: What Are the Pitfalls of Score Construction, Reliability, and Validation? A General Methodological Approach Applied in Cattle.

作者信息

Buczinski Sébastien, Boccardo Antonio, Pravettoni Davide

机构信息

Département des Sciences Cliniques, Faculté de Médecine Vétérinaire, Université de Montréal, Saint-Hyacinthe, QC J2S 2M2, Canada.

Centre d'Expertise et de Recherche Clinique en Santé et Bien-Etre Animal (CERCL), Faculté de Médecine Vétérinaire, Université de Montréal, Saint-Hyacinthe, QC J2S 2M2, Canada.

出版信息

Animals (Basel). 2021 Nov 13;11(11):3244. doi: 10.3390/ani11113244.

Abstract

Clinical scores are commonly used for cattle. They generally contain a mix of categorical and numerical variables that need to be assessed by scorers, such as farmers, animal caretakers, scientists, and veterinarians. This article examines the key concepts that need to be accounted for when developing the test for optimal outcomes. First, the target condition or construct that the scale is supposed to measure should be defined, and if possible, an adequate proxy used for classification should be determined. Then, items (e.g., clinical signs) of interest that are either caused by the target condition (reflective items) or that caused the target condition (formative items) are listed, and reliable items (inter and intra-rater reliability) are kept for the next step. A model is then developed to determine the relative weight of the items associated with the target condition. A scale is then built after validating the model and determining the optimal threshold in terms of sensitivity (ability to detect the target condition) and specificity (ability to detect the absence of the target condition). Its robustness to various scenarios of the target condition prevalence and the impact of the relative cost of false negatives to false positives can also be assessed to tailor the scale used based on specific application conditions.

摘要

临床评分常用于牛。它们通常包含分类变量和数值变量的混合,需要由评分者进行评估,如农民、动物饲养员、科学家和兽医。本文探讨了在开发测试以实现最佳结果时需要考虑的关键概念。首先,应定义量表旨在测量的目标状况或结构,并且如果可能的话,应确定用于分类的适当替代指标。然后,列出由目标状况引起的(反映性项目)或导致目标状况的(构成性项目)感兴趣的项目(如临床体征),并保留可靠的项目(评分者间和评分者内信度)用于下一步。然后开发一个模型来确定与目标状况相关的项目的相对权重。在验证模型并根据敏感性(检测目标状况的能力)和特异性(检测目标状况不存在的能力)确定最佳阈值后,构建一个量表。还可以评估其对目标状况流行率的各种情况的稳健性以及假阴性与假阳性相对成本的影响,以便根据特定应用条件调整所使用的量表。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13b6/8614512/c4cc25c1f548/animals-11-03244-g001.jpg

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