背景:认知筛查工具在临床实践中广泛用于筛查与年龄相关的认知障碍和痴呆。众所周知,这些工具的考试成绩受年龄和教育程度的影响,导致这些因素的原始分数的常规校正。尽管这些更正是常见的做法,有证据表明,校正后的分数在歧视方面可能比原始分数更差。
目的:为了解决痴呆症研究领域正在进行的辩论,我们评估了更正对歧视的影响,特异性,和敏感性的蒙特利尔认知评估测试在意大利,无论是对于总人口还是年龄和教育阶层。
方法:我们在年龄方面创建了意大利常住人口的现实模型,教育,认知障碍和考试成绩,并进行了模拟研究。
结果:我们证实,在将认知障碍患者与没有认知障碍的个体(曲线下面积分别为0.947和0.923)进行区分时,原始分数的区分表现高于校正分数。根据总人口确定的阈值,原始评分显示,高风险年龄教育组的敏感性较高,低风险组的特异性较高.相反,校正后的分数显示出跨人口阶层的统一敏感性和特异性,因此,某些年龄教育群体的表现更好。
结论:由于变量之间的潜在因果关系,原始评分和校正评分显示出不同的表现。每种方法都有优点和缺点,原始分数和校正分数之间的最佳选择取决于从业者和决策者的目标和偏好。
BACKGROUND: Cognitive screening tools are widely used in clinical practice to screen for age-related cognitive impairment and dementia. These tools\' test scores are known to be influenced by age and education, leading to routine correction of raw scores for these factors. Despite these
corrections being common practice, there is evidence suggesting that corrected scores may perform worse in terms of discrimination than raw scores.
OBJECTIVE: To address the ongoing debate in the field of dementia research, we assessed the impact of the
corrections on discrimination, specificity, and sensitivity of the Montreal Cognitive Assessment test in Italy, both for the overall population and across age and education strata.
METHODS: We created a realistic model of the resident population in Italy in terms of age, education, cognitive impairment and test scores, and performed a simulation study.
RESULTS: We confirmed that the discrimination performance was higher for raw scores than for corrected scores in discriminating patients with cognitive impairment from individuals without (areas under the curve of 0.947 and 0.923 respectively). With thresholds determined on the overall population, raw scores showed higher sensitivities for higher-risk age-education groups and higher specificities for lower-risk groups. Conversely, corrected scores showed uniform sensitivity and specificity across demographic strata, and thus better performance for certain age-education groups.
CONCLUSIONS: Raw and corrected scores show different performances due to the underlying causal relationships between the variables. Each approach has advantages and disadvantages, the optimal choice between raw and corrected scores depends on the aims and preferences of practitioners and policymakers.