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广泛使用的神经心理测验组合中嵌入的用于评估表现效度的经验性推导算法:在诉讼中的创伤性脑损伤患者中的验证

Empirically derived algorithm for performance validity assessment embedded in a widely used neuropsychological battery: Validation among TBI patients in litigation.

作者信息

Bar-Hen Moran, Doniger Glen M, Golzad Mehrdad, Geva Naomi, Schweiger Avraham

机构信息

a Department of Psychology , Ben-Gurion University of the Negev , Beer Sheva , Israel.

b NeuroTrax Corp. , Gaithersburg , MD , USA.

出版信息

J Clin Exp Neuropsychol. 2015;37(10):1086-97. doi: 10.1080/13803395.2015.1078294. Epub 2015 Sep 1.

Abstract

INTRODUCTION

Validity of neuropsychological assessment depends, inter alia, on the cooperation of the examinee, requiring separate assessment. Stand-alone tests devised for detecting negative response bias (NRB) are exposed to potential threats to their validity. In this study, an algorithm was developed for assessing NRB within a standardized, computerized neuropsychological battery (NeuroTrax), making it difficult to detect and circumvent.

METHOD

Data were collected from the archived medical records of 75 outpatients with mild to moderate head injury, all in litigation. Participants were classified as low or high likelihood for NRB, using a known test for effort assessment (Test of Memory Malingering).

RESULTS

Variables judged to be prone for exaggeration and showing large differences between the groups were entered into a logistic regression analysis. The resulting formula exhibited high specificity (98.0%) and sensitivity (87.5%), classifying correctly 94% of the cases.

CONCLUSION

It is suggested that the algorithm developed empirically using scores on the NeuroTrax computerized battery can be a useful tool for assessing effort. This algorithm should resist threats to its validity and can be automatically computed while assessing a range of cognitive skills.

摘要

引言

神经心理学评估的有效性尤其取决于受测者的配合程度,这需要进行单独评估。为检测负性反应偏差(NRB)而设计的独立测试面临着对其有效性的潜在威胁。在本研究中,开发了一种算法,用于在标准化的计算机化神经心理学测试组(NeuroTrax)中评估NRB,使其难以被检测和规避。

方法

从75名轻度至中度头部受伤且均在诉讼中的门诊患者的存档病历中收集数据。使用一种已知的努力程度评估测试(记忆伪装测试)将参与者分为NRB可能性低或高的组。

结果

将被判定易于夸大且两组间存在较大差异的变量纳入逻辑回归分析。所得公式显示出高特异性(98.0%)和敏感性(87.5%),正确分类了94%的病例。

结论

建议使用NeuroTrax计算机化测试组的分数通过经验开发的算法可成为评估努力程度的有用工具。该算法应能抵御对其有效性的威胁,并且在评估一系列认知技能时可自动计算。

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