Literature DB >> 26882178

Indicators of suboptimal performance embedded in the Wechsler Memory Scale-Fourth Edition (WMS-IV).

Zita Bouman1,2, Marc P H Hendriks1,2, Ben A Schmand3,4, Roy P C Kessels2,5, Albert P Aldenkamp1,6,7,8.   

Abstract

INTRODUCTION: Recognition and visual working memory tasks from the Wechsler Memory Scale-Fourth Edition (WMS-IV) have previously been documented as useful indicators for suboptimal performance. The present study examined the clinical utility of the Dutch version of the WMS-IV (WMS-IV-NL) for the identification of suboptimal performance using an analogue study design.
METHOD: The patient group consisted of 59 mixed-etiology patients; the experimental malingerers were 50 healthy individuals who were asked to simulate cognitive impairment as a result of a traumatic brain injury; the last group consisted of 50 healthy controls who were instructed to put forth full effort.
RESULTS: Experimental malingerers performed significantly lower on all WMS-IV-NL tasks than did the patients and healthy controls. A binary logistic regression analysis was performed on the experimental malingerers and the patients. The first model contained the visual working memory subtests (Spatial Addition and Symbol Span) and the recognition tasks of the following subtests: Logical Memory, Verbal Paired Associates, Designs, Visual Reproduction. The results showed an overall classification rate of 78.4%, and only Spatial Addition explained a significant amount of variation (p < .001). Subsequent logistic regression analysis and receiver operating characteristic (ROC) analysis supported the discriminatory power of the subtest Spatial Addition. A scaled score cutoff of <4 produced 93% specificity and 52% sensitivity for detection of suboptimal performance.
CONCLUSION: The WMS-IV-NL Spatial Addition subtest may provide clinically useful information for the detection of suboptimal performance.

Entities:  

Keywords:  Assessment; effort indicators; logistic regression.; malingering; suboptimal performance

Mesh:

Year:  2016        PMID: 26882178     DOI: 10.1080/13803395.2015.1123226

Source DB:  PubMed          Journal:  J Clin Exp Neuropsychol        ISSN: 1380-3395            Impact factor:   2.475


  1 in total

1.  A machine learning-based linguistic battery for diagnosing mild cognitive impairment due to Alzheimer's disease.

Authors:  Sylvester Olubolu Orimaye; Karl Goodkin; Ossama Abid Riaz; Jean-Maurice Miranda Salcedo; Thabit Al-Khateeb; Adeola Olubukola Awujoola; Patrick Olumuyiwa Sodeke
Journal:  PLoS One       Date:  2020-03-05       Impact factor: 3.240

  1 in total

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