Recently, a multilevel analytic approach called Main Concept, Sequencing, and Story Grammar (MSSG) was presented along with preliminary normative information. MSSG analyses leverage the strong psychometrics and rich procedural knowledge of both main concept analysis and story grammar component coding, complementing it with easy-to-obtain sequencing information for a rich understanding of discourse informativeness and macrostructure. This study is the next critical step for demonstrating the clinical usefulness of MSSG’s six variables (main concept composite, sequencing, main concept+sequencing, essential story grammar components, total episodic components, and episodic complexity) for persons with aphasia (PWAs). We present descriptive statistical information for MSSG variables for a large sample of PWAs and compare their performance to a large sample of persons not brain injured (PNBIs). We observed significant differences between PWAs and PNBIs for all MSSG variables. These differences occurred at the omnibus group level and for each aphasia subtype, even for PWAs with very mild impairment that is not detected with standardized aphasia assessment. Differences between PWAs and PNBIs were also practically significant, with medium to large effect sizes observed for nearly all aphasia subtypes and MSSG variables. This work deepens our understanding of discourse informativeness and macrostructure in PWAs and further develops an efficient tool for research and clinical use. Future research should investigate ways to expand MSSG analyses and to improve sensitivity and specificity.

中文翻译：

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大量失语症患者中的灰姑娘叙述的主要概念，测序和故事语法分析

最近，提出了一种称为主要概念，排序和故事语法（MSSG）的多层次分析方法，以及初步的规范信息。MSSG分析利用了主要概念分析和故事语法组件编码方面的强大心理计量学和丰富的程序知识，并通过易于获得的排序信息对其进行了补充，以丰富对话语信息性和宏观结构的理解。这项研究是证明MSSG对失语症患者（PWA）的六个变量（主要概念复合，排序，主要概念+排序，基本故事语法组成，总情节组成和情节复杂性）的临床实用性的下一个关键步骤。我们为大量PWA提供MSSG变量的描述性统计信息，并将其性能与未被脑损伤的人（PNBI）的大量样本进行比较。我们观察到所有MSSG变量的PWA和PNBI之间存在显着差异。这些差异发生在综合组水平和每种失语症亚型上，即使对于轻度受损的PWA（通过标准化失语症评估也无法检测到）也是如此。PWA和PNBI之间的差异实际上也很明显，几乎所有失语症亚型和MSSG变量均观察到中到大型效应。这项工作加深了我们对PWA中话语信息性和宏观结构的理解，并进一步开发了一种用于研究和临床使用的有效工具。