记忆集成语言的多变量实用性和变化测试
John-Christopher A Finley1, Mira I Leese2, Jarett E Roseberry3
1Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Applied neuropsychology. Adult
|July 29, 2024
概括
结合记忆综合语言测试 (MIL) 和变化测试缩写指数 (MCT-AI) 显著改善了神经心理评估中无效表现的检测. 这种配对方法可以提高准确度和灵敏度,同时保持高特异性.
科学领域:
- 神经心理学 神经心理学
- 认知评估 认知评估
- 性能有效性测试 性能有效性测试
背景情况:
- 基于Web的性能验证测试 (PVTs),如记忆集成语言测试 (MIL) 和变化测试缩写指数 (MCT-AI) 显示出个别的承诺.
- 在神经心理评估中,检测不良表现至关重要.
研究的目的:
- 评估MIL和MCT-AI在混合神经精神病样本中检测无效表现的联合有效性.
- 要确定与独立使用相比,使用这些PVT一起是否会提高分类准确性.
主要方法:
- 评估了129名成年门诊患者,并将其分为有效 (n=104) 或无效 (n=25) 绩效组.
- 结合使用MIL (切割得分≤41) 和MCT-AI (切割得分≥1.05).
- 使用曲线下的面积 (AUC) 测量了分类准确性.
主要成果:
- 结合使用MIL和MCT-AI,AUC达到0.84 (95%CI:0.75,0.93).
- 在联合使用时,灵敏度从0.10-.31增加到0.70,而特异性保持在≥90.0.
- 两个测试都失败,与只失败一次相比,认知分数显著降低.
结论:
- 结合使用MIL和MCT-AI是一种有效的策略,用于识别神经心理评估中的无效表现.
- 这些PVT的配对符合指导方针,该指导方针建议对性能有效性评估采取多种措施.
- 这种方法提高了神经心理评估结果的可靠性.
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