Suppr超能文献

意大利人群中修订版简短视觉空间记忆测试基于回归的常模及其在多发性硬化症患者中的应用。

Regression-Based Norms for the Brief Visuospatial Memory Test-Revised in Italian population and application in MS patients.

作者信息

Argento Ornella, Smerbeck Audrey, Pisani Valerio, Magistrale Giuseppe, Incerti Chiara C, Caltagirone Carlo, Benedict Ralph H B, Nocentini Ugo

机构信息

a Neurology and Neurorehabilitation Unit , I.R.C.C.S. "Santa Lucia" Foundation , Rome , Italy.

b Psychology Department , Rochester Institute of Technology , New York , NY , USA.

出版信息

Clin Neuropsychol. 2016 Jan-Dec;30(sup1):1469-1478. doi: 10.1080/13854046.2016.1183713. Epub 2016 May 13.

Abstract

OBJECTIVE

The Brief Visuospatial Memory Test-Revised (BVMT-R) is one of the most widely used tests for the assessment of learning and memory in the visual/spatial domain. The aim of this study was to use multiple regression to derive normative data for the use of BVMT-R in an Italian population.

METHOD

We employed a regression-based norms procedure to maximally utilize a relatively small sample while controlling for a variety of demographic factors in addition to age. Additionally, we used these norms to compare the performance of Italian healthy controls with patients diagnosed with multiple sclerosis (MS), thereby providing evidence of the method's validity.

RESULTS

A total of 200 healthy volunteers and 70 MS patients participated in this study and completed the BVMT-R according to the published procedures. Regression-based norms were generated for the Italian sample and are presented herein.

CONCLUSIONS

Using these norms, the performance of the MS patients was found to be significantly worse than that of the controls.

摘要

目的

简明视觉空间记忆测试修订版(BVMT-R)是评估视觉/空间领域学习与记忆最广泛使用的测试之一。本研究的目的是运用多元回归得出BVMT-R在意大利人群中的常模数据。

方法

我们采用基于回归的常模程序,在控制除年龄外的多种人口统计学因素的同时,最大程度利用相对较小的样本。此外,我们使用这些常模比较意大利健康对照者与被诊断为多发性硬化症(MS)患者的表现,从而提供该方法有效性的证据。

结果

共有200名健康志愿者和70名MS患者参与本研究,并按照已发表的程序完成了BVMT-R。为意大利样本生成了基于回归的常模,并在此呈现。

结论

使用这些常模发现,MS患者的表现明显比对照者差。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验