Department of Neurology, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA.
J Int Neuropsychol Soc. 2010 Jul;16(4):679-86. doi: 10.1017/S1355617710000470. Epub 2010 May 5.
Demographically adjusted norms generally enhance accuracy of inferences based on neuropsychological assessment. However, we hypothesized that demographic corrections diminish predictive accuracy for real-world activities with absolute cognitive demands. Driving ability was assessed with a 45-minute drive along a standardized on-road route in participants aged 65+ (24 healthy elderly, 26 probable Alzheimer's disease, 33 Parkinson's disease). Neuropsychological measures included: Trail-Making A and B, Complex Figure, Benton Visual Retention, and Block Design tests. A multiple regression model with raw neuropsychological scores was significantly predictive of driving errors (R2 = .199, p = .005); a model with demographically adjusted scores was not (R2 = .113, p = .107). Raw scores were more highly correlated with driving errors than were adjusted scores for each neuropsychological measure, and among healthy elderly and Parkinson's patients. When predicting real-world activities that depend on absolute levels of cognitive abilities regardless of demographic considerations, predictive accuracy is diminished by demographic corrections.
人口统计学调整的标准通常可以提高基于神经心理学评估的推论的准确性。然而，我们假设人口统计学校正会降低对具有绝对认知要求的现实世界活动的预测准确性。驾驶能力是通过让参与者在 65 岁以上的年龄（24 名健康老年人，26 名可能患有阿尔茨海默病，33 名帕金森病）沿着标准化的道路上行驶 45 分钟来评估的。神经心理学测试包括：连线测试 A 和 B、复杂图形、本顿视觉保持和积木设计测试。原始神经心理学评分的多元回归模型对驾驶错误具有显著的预测性（R2=.199，p=.005）；而经过人口统计学校正的评分模型则不具有预测性（R2=.113，p=.107）。对于每个神经心理学测试以及健康老年人和帕金森病患者来说，原始分数与驾驶错误的相关性都高于校正分数。当预测依赖于绝对认知能力水平而不考虑人口统计学因素的现实世界活动时，人口统计学校正会降低预测准确性。
