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      Brief and cost-effective tool for assessing verbal learning in multiple sclerosis: Comparison of the Rey Auditory Verbal Learning Test (RAVLT) to the California Verbal Learning Test – II (CVLT-II)

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      Journal of the Neurological Sciences
      Elsevier BV

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          Abstract

          The Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) is a common cognitive screening tool. However, administration and scoring can be time-consuming, and its use of proprietary subtests like the California Verbal Learning Test – II (CVLT-II) is financially limiting. Use of the non-proprietary Rey Auditory Verbal Learning Test (RAVLT) may be provide a valid alternative. To compare the RAVLT and CVLT-II in terms of diagnostic accuracy for detecting cognitive impairment, and to determine optimal cut-scores for the RAVLT. 100 participants with MS completed the five learning trials from the RAVLT and CVLT-II. Receiver operating characteristic analyses were used to compare the measures’ sensitivities, specificities, positive predictive values (PPV) and negative predictive values (NPV), and to identify optimal cut-scores. Using a criterion of 1.5 SD below the normative sample mean, the RAVLT showed fair to good ( κs = .21-.41) agreement with the CVLT-II. A cut-score of 12 on Trials 1+2 of the RAVLT showed fair sensitivity (75%) and specificity (76%) and did not differ significantly from the CVLT-II ( p >.05). Performance on initial learning trials of the RAVLT may provide a brief, valid, and cost-effective alternative to the CVLT-II for screening verbal learning impairments in MS.

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          Author and article information

          Journal
          Journal of the Neurological Sciences
          Journal of the Neurological Sciences
          Elsevier BV
          0022510X
          May 2019
          May 2019
          : 400
          : 104-109
          Article
          10.1016/j.jns.2019.03.016
          6475461
          30913522
          701b347d-0f5a-4472-8808-586001cb4861
          © 2019

          https://www.elsevier.com/tdm/userlicense/1.0/

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