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记忆整合语言与找零测试的多变量效用

Multivariable utility of the Memory Integrated Language and Making Change Test.

作者信息

Finley John-Christopher A, Leese Mira I, Roseberry Jarett E, Hill S Kristian

机构信息

Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

Department of Psychology, Rosalind Franklin University of Medicine and Science, Chicago, IL, USA.

出版信息

Appl Neuropsychol Adult. 2024 Jul 29:1-8. doi: 10.1080/23279095.2024.2385439.

DOI:10.1080/23279095.2024.2385439
PMID:39073594
Abstract

Recent reports indicate that the Memory Integrated Language Test (MIL) and Making Change Test Abbreviated Index (MCT-AI), two web-based performance validity tests (PVTs), have good sensitivity and specificity when used independently. This study investigated whether using these PVTs together could improve the detection of invalid performance in a mixed neuropsychiatric sample. Participants were 129 adult outpatients who underwent a neuropsychological evaluation and were classified into valid ( = 104) or invalid ( = 25) performance groups based on several commonly used PVTs. Using cut scores of ≤41 on the MIL and ≥1.05 on the MCT-AI together enhanced classification accuracy, yielding an area under the curve of .84 (95% CI: .75, .93). As compared to using the MIL and MCT-AI independently, the combined use increased the sensitivity from .10-.31 to.70 while maintaining ≥.90 specificity. Findings also indicated that failing either the MIL or MCT-AI was associated with somewhat lower cognitive test scores, but failing both was associated with markedly lower scores. Overall, using the MIL and MCT-AI together may be an effective way to identify invalid test performance during a neuropsychological evaluation. Furthermore, pairing these tests is consistent with current practice guidelines to include multiple PVTs in a neuropsychological test battery.

摘要

最近的报告表明,记忆综合语言测试(MIL)和找零测试简化指数(MCT-AI)这两种基于网络的效标效度测试(PVT)单独使用时具有良好的敏感性和特异性。本研究调查了同时使用这些PVT是否能提高在混合神经精神样本中对无效表现的检测能力。参与者为129名成年门诊患者,他们接受了神经心理学评估,并根据几种常用的PVT被分为有效(=104)或无效(=25)表现组。将MIL的临界值设定为≤41且MCT-AI的临界值设定为≥1.05,共同提高了分类准确性,曲线下面积为0.84(95%CI:0.75,0.93)。与单独使用MIL和MCT-AI相比,联合使用将敏感性从0.10 - 0.31提高到了0.70,同时保持特异性≥0.90。研究结果还表明,MIL或MCT-AI其中一项未通过与认知测试分数略低有关,但两项都未通过则与分数显著降低有关。总体而言,同时使用MIL和MCT-AI可能是在神经心理学评估中识别无效测试表现的有效方法。此外,将这些测试配对符合当前的实践指南,即在神经心理测试组合中纳入多个PVT。

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