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How Does Severity of Aphasia Influence Individual Responsiveness to Rehabilitation? Using Big Data to Understand Theories of Aphasia Rehabilitation

机译:失语症的严重程度如何影响个人对康复的反应?利用大数据理解失语症康复理论

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Our ability to make great progress in delivering, optimizing, and predicting rehabilitation outcomes for individuals with aphasia is challenged by factors that influence rehabilitation outcomes. These include patient demographic factors such as age, education, and neurologic factors such as time poststroke, the site and size of the lesion, and the resulting severity of language impairment. Also variable across individuals is the type of treatment and its duration and intensity. This article examines the utility of big data analysis for understanding one of these factors, severity of impairment, and how individual responsiveness to rehabilitation is influenced by a patient's severity of language and cognitive impairment(s). Using examples from two studies and a larger data set, we show that when rehabilitation is tailored to an individual's specific level of impairment, severe and mild patients both show improvements in accuracy and latency. Furthermore, more severe patients tend to show substantial gains on targeted rehabilitation tasks as well as on standardized tests. These results provide support for recent reviews of aphasia rehabilitation studies in concluding that systematic aphasia rehabilitation is indeed effective, and importantly, severity is not a negative prognostic indicator for successful outcomes.
机译:我们在为失语症患者提供,优化和预测康复结果方面取得重大进展的能力受到影响康复结果的因素的挑战。这些包括患者人口统计学因素(例如年龄,教育程度)和神经系统因素(例如中风后的时间,病变部位和大小以及语言障碍的严重程度)。个体之间的差异还在于治疗的类型,持续时间和强度。本文探讨了使用大数据分析来理解以下因素之一,损害的严重性以及患者对语言的严重程度和认知损害如何影响个人对康复的反应。使用来自两项研究的示例和更大的数据集,我们表明,根据个人的特定损伤水平定制康复服务时,重度和轻度患者均显示出准确性和潜伏期的改善。此外,更严重的患者倾向于在有针对性的康复任务以及标准化测试方面显示出可观的收益。这些结果为失语症康复研究的最新综述提供了支持,认为系统性失语症康复确实有效,重要的是,严重程度并非成功预后的阴性预后指标。

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