Separate scoring algorithms for specific identification priorities optimize the screening properties of the Screening Tool for Autism in Toddlers (STAT).
Separate scoring algorithms for specific identification priorities optimize the screening properties of the Screening Tool for Autism in Toddlers (STAT).
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针对特定识别优先级的单独评分算法优化了幼儿自闭症筛查工具（STAT）的筛查属性。
DOI：
10.1002/aur.2799
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发表时间：
2022-11
期刊：
影响因子：
4.7
通讯作者：
Stone, Wendy L.
中科院分区：
文献类型：
--
作者：
Attar, Shana M.;Ibanez, Lisa, V;Stone, Wendy L.
幼儿自闭症筛查工具 (STAT) 是一项经过验证的 2 期自闭症谱系障碍 (ASD) 筛查措施，需要 20 分钟才能完成，包含 12 个基于游戏的项目，并根据特定标准进行评分。本研究考察了扩展版本 (STAT-E)，其中包括考官在评分算法中对儿童社交参与 (SE) 和非典型行为 (AB) 的主观评分。样本包括 238 名 24-35 个月大的儿童。 STAT-E 评估员的 ASD 经验有限，为了模仿社区非专家对其的使用，并使用可扩展的基于网络的平台进行了培训。诊断评估由对 STAT-E 结果不知情的临床专家完成。当使用原始 STAT 评分算法与包含 SE 和 AB 评级的新算法进行评分时，使用逻辑回归、ROC 曲线以及分类矩阵和指标来确定 STAT-E 的筛选特性。纳入 SE 和 AB 评级显着改善了阳性风险分类，但特异性下降，这表明使用原始 STAT 评分算法的 STAT-E 优化了特异性，而具有两个新评级的 STAT-E 评分算法优化了阳性风险分类。在 STAT 上使用多种评分算法可以提高不同背景下的筛查准确性，并且可扩展的基于网络的教程可能是增加可以管理 STAT 的社区提供者数量的途径，并有助于提高自闭症筛查率。检测幼儿自闭症可以及时获得专门的早期干预服务。新的评分系统包含了幼儿自闭症筛查工具 (STAT) 的两个新评级，当由接受过网络教程培训的新手 STAT 评估员进行管理时，可以显着增加正确识别出 ASD 风险的儿童数量。使用基于网络的培训和非专家提供者有可能增加能够管理 STAT 的社区提供者的数量，并有助于提高 ASD 筛查和服务获取率。
The Screening Tool for Autism in Toddlers (STAT) is a validated stage-2 Autism Spectrum Disorder (ASD) screening measure that takes 20 minutes to administer and comprises 12 play-based items that are scored according to specific criteria. This study examines an expanded version (STAT-E) that includes the examiner’s subjective ratings of children’s social engagement (SE) and atypical behaviors (AB) in the scoring algorithm. The sample comprised 238 children who were 24-35 months old. The STAT-E assessors had limited ASD experience, to mimic its use by community-based non-specialists, and were trained using a scalable web-based platform. A diagnostic evaluation was completed by clinical experts who were blind to the STAT-E results. Logistic regression, ROC curves, and classification matrices and metrics were used to determine the screening properties of STAT-E when scored using the original STAT scoring algorithm vs. a new algorithm that included the SE and AB ratings. Inclusion of the SE and AB ratings improved positive risk classification appreciably, while the specificity declined, suggesting that the STAT-E using the original STAT scoring algorithm optimizes specificity, while the STAT-E scoring algorithm with the two new ratings optimizes the positive risk classification. Using multiple scoring algorithms on the STAT may provide improved screening accuracy for diverse contexts, and a scalable web-based tutorial may be a pathway for increasing the number of community providers who can administer the STAT and contribute toward increased rates of autism screening. Detecting autism in young children allows for timely access to specialized early intervention services. A new scoring system that incorporates two new ratings for the Screening Tool for Autism in Toddlers (STAT) significantly increased the number of children correctly identified at ASD risk when administered by novice STAT assessors who were trained using a web-based tutorial. Using web-based training and non-expert providers has the potential to increase the number of community providers who can administer the STAT and contribute toward increased rates of ASD screening and service access.
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作者：
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DOI：
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发表时间：
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