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Updated: Jun 13, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

[智利老年人口中微型精神状态检查 (MMSE) 规范数据的发展:多城市研究]

Matías Molina-Donoso¹, Jorge González-Hernández², Carolina Delgado³

¹Centro de Gerociencia, Salud Mental y Metabolismo, Universidad de Chile, Santiago, Chile.

Revista medica de Chile

|September 13, 2024

概括

这项研究提供了智利老年人的迷你精神状态考试 (MMSE) 的规范分数. 这些根据年龄和教育程度进行调整的得分有助于解释认知功能评估.

科学领域:

老年学是一门学科.
神经心理学神经心理学
认知评估认知评估

背景情况:

迷你精神状态检查 (MMSE) 是评估老年人认知衰退的关键工具.
现有的西班牙语版本缺少智利的特定人口规范数据.
之前在智利进行的验证研究没有建立大规模人口标准.

研究的目的:

为智利老年人建立适合年龄和教育水平的规范性MMSE分数.
为智利的认知评估提供最新,可靠的基准.
为了促进智利老年人对MMSE结果的准确解释.

主要方法:

调查了一大批1199名智利成年人,年龄在50-91岁之间.
数据收集发生在多个研究中心.
规范性得分是使用百分位数排名计算的.

主要成果:

基于百分位数的规范分数为不同的年龄组 (50-64,65-74,75-91) 呈现.
评分是根据教育程度 (1-12岁与13岁以上) 分层的.
这些结果提供了MMSE业绩的详细人口统计细分.

结论:

开发的MMSE规范分数对智利的临床实践有价值.
这些标准将改善智利老年人潜在障碍认知功能的解释.
这项研究解决了对文化和人口统计学相关的认知评估工具的关键需求.

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