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Evidence for added value of baseline testing in computer-based cognitive assessment

机译:基于计算机的认知评估中基线测试增值的证据

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Context: Large-scale baseline cognitive assessment for individuals at risk for concussion is a common part of the protocol for concussion-surveillance programs, particularly in sports. Baseline cognitive testing is also being conducted in US military service members before deployment. Recently, the incremental validity of large-scale baseline cognitive assessment has been questioned. Objective: To examine the added value of baseline cognitive testing in computer-based neuropsychological assessment by comparing 2 methods of classifying atypical performance in a presumed healthy sample. Design: Cross-sectional study. Setting: Military base. Patients or Other Participants: Military service members who took the Automated Neuropsychological Assessment Matrix (ANAM) before and after deployment (n = 8002). Main Outcome Measure(s): Rates of atypical performance in this healthy, active-duty sample were determined first by comparing postdeployment scores with a military normative database and then with each individual's personal baseline performance using a reliable change index. Results: Overall rates of atypical performance were comparable across these 2 methods. However, these methods were highly discordant in terms of which individuals were classified as atypical. When norm-referenced methods were used, 2.6% of individuals classified as normal actually demonstrated declines from baseline. Further, 65.7% of individuals classified as atypical using norm-referenced scores showed no change from baseline (ie, potential false-positive findings). Conclusions: Knowing an individual's baseline performance is important for minimizing potential false-positive errors and reducing the risks and stresses of misdiagnosis.
机译:背景:对有脑震荡危险的人进行大规模的基线认知评估是脑震荡监视程序(特别是在体育运动中)的协议的共同部分。部署之前,美国军人还进行了基准认知测试。最近,人们对大型基线认知评估的增量有效性提出了质疑。目的:通过比较对假定健康样本中非典型表现进行分类的两种方法,研究基线认知测试在基于计算机的神经心理学评估中的附加价值。设计:横断面研究。地点:军事基地。患者或其他参与者:在部署前后接受自动神经心理评估矩阵(ANAM)的军事部门成员(n = 8002)。主要观察指标:首先通过将部署后得分与军事规范数据库进行比较,然后使用可靠的变化指数与每个人的个人基线表现进行比较,来确定此健康,现役样本中的非典型表现率。结果:这两种方法的总体非典型表现率相当。但是,这些方法在将哪些个体分类为非典型个体方面非常不一致。当使用规范参考方法时,被归类为正常的个体中有2.6%实际上表现出比基线有所下降。此外,使用规范参考评分被分类为非典型的个体中有65.7%的人与基线相比没有变化(即潜在的假阳性结果)。结论:了解个人的基线表现对于最大程度地减少潜在的假阳性错误,减少误诊的风险和压力非常重要。

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