Project I: Definition, Classification, and Risk Prediction

摘要 项目1 项目一：风险的定义、分类和预测 词级阅读障碍(即阅读困难)和特定阅读理解障碍(SRCD)是两种 重要的公共卫生问题，估计阅读率在3%到20%之间 残疾人比例为8%至10%，SRCD为8%至10%。该项目的长期目标是大幅增加 关于这些学习障碍的性质的可复制的知识，并在工具中实施这种知识 这可能会改善有学习障碍的个人及其家庭的结果。现有 以单一指标为优先顺序的阅读障碍的定义(例如，解码不佳，对 说明/干预)显示一致性和纵向稳定性较差。然而，一种可操作的 从阅读障碍的多变量模型得出的定义显示出更好的表现，通过 综合多种指标。具体目标1是实施阅读障碍的多变量模型作为工具 可在个人级别使用，以预测风险、帮助识别和估计概率 关于重要的功能结果，例如使用辅助技术的可能价值。基于模型的 将使用荟萃分析、模拟和对新数据的应用来生成和测试预测模型， 包括源自人工智能和贝叶斯推理的模型。具体目标2是确定 阅读障碍的神经生物学和行为领先指标，可能对预测风险有价值，有助于 识别，或在预测功能上的重大结果。尽管两国之间存在着既定的关系 基于大脑的结构(结构和功能)以及语言和识字结构，它在很大程度上是 不知道基于大脑的结构是最好地概念化为原因、结果还是仅仅 语言和识字结构的关联性。潜在变化分数建模，动态系统的一种形式 建模，是一种最先进的方法，用于测试假设领先、滞后或没有直接影响的替代模型 两种结构之间的关系，而不仅仅是它们之间的相互关系的发展。具体目标3是进一步 理解特定阅读理解障碍的性质以及如何最好地预测它 并确认了身份。我们打算进一步探讨这一现象的性质，并使用我们将采取的方法 我在具体目标1中概述了开发一个预测特定阅读理解风险的模型 残疾。最后，具体目标4是尽可能征聘、研究、分析和报告分类结果 对于历史上研究不足和服务不足的人群(例如，英语学习者、居住在 贫穷、男性和女性、种族/族裔认同)，因为我们努力实现前三个具体目标。

ABSTRACT Project 1 Project I: Definition, Classification, and Prediction of Risk Word-level reading disability (i.e., dyslexia) and specific reading comprehension disability (SRCD) are two important public health problems, with estimates of prevalence ranging from 3 to 20 percent for reading disability and 8 to 10 percent for SRCD. The long-term objective of this project is to substantially increase replicable knowledge about the nature of these learning disabilities and to implement this knowledge in tools that potentially can improve the outcomes of individuals with learning disabilities and their families. Existing definitions of reading disability that prioritize a single indicator (e.g., poor decoding, inadequate response to instruction/intervention) show poor levels of agreement and longitudinal stability. However, an operational definition derived from a multivariate model of reading disability shows substantially better performance by combining multiple indicators. Specific aim 1 is to implement a multivariate model of reading disability as a tool that can be used at the level of the individual to predict risk, aid in identification, and estimate probabilities about functionally-significant outcomes such as the likely value of using assistive technology. Model-based meta-analysis, simulation, and application to new data will be used to generate and test prediction models, including models derived from artificial intelligence and Bayesian inference. Specific aim 2 is to identify neurobiological and behavioral leading indicators of dyslexia that may have value for predicting risk, aiding identification, or in predicting functionally significant outcomes. Although established relations exist between brain-based constructs (both structural and functional) and language and literacy constructs, it is largely unknown whether the brain-based constructs are best conceptualized as causes, consequences, or mere correlates of the language and literacy constructs. Latent change score modeling, a form of dynamic systems modeling, is a state-of-the-science approach to test alternative models that posit leading, lagging, or no direct relations between two constructs beyond their mere correlated development. Specific aim 3 is to further understanding about the nature of specific reading comprehension disability and how it best can be predicted and identified. We intend to further explore the nature of this phenomenon and to use the approach we will have outlined in specific aim 1 to develop a model for predicting risk of specific reading comprehension disability. Finally specific aim 4 is to recruit, study, analyze, and report disaggregated results where possible for historically understudied and underserved populations (e.g., English Language Learners, families living in poverty, males and females, racial/ethnic identity) as we work to achieve the previous three specific aims.