• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

一种新型移动自闭症风险评估的临床评估

Clinical Evaluation of a Novel and Mobile Autism Risk Assessment.

作者信息

Duda Marlena, Daniels Jena, Wall Dennis P

机构信息

Division of Systems Medicine, Department of Pediatrics, Stanford University, 1265 Welch Road, Stanford, CA, 94305, USA.

Department of Biomedical Data Science, Stanford University, 1265 Welch Road, Stanford, CA, 94305, USA.

出版信息

J Autism Dev Disord. 2016 Jun;46(6):1953-1961. doi: 10.1007/s10803-016-2718-4.

DOI:10.1007/s10803-016-2718-4
PMID:26873142
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4860199/
Abstract

The Mobile Autism Risk Assessment (MARA) is a new, electronically administered, 7-question autism spectrum disorder (ASD) screen to triage those at highest risk for ASD. Children 16 months-17 years (N = 222) were screened during their first visit in a developmental-behavioral pediatric clinic. MARA scores were compared to diagnosis from the clinical encounter. Participant median age was 5.8 years, 76.1 % were male, and most participants had an intelligence/developmental quotient score >85; 69 of the participants (31 %) received a clinical diagnosis of ASD. The sensitivity of the MARA in detecting ASD was 89.9 % [95 % CI = 82.7-97]; the specificity was 79.7 % [95 % CI = 73.4-86.1]. In a high-risk clinical setting, the MARA shows promise as a screen to distinguish ASD from other developmental/behavioral disorders.

摘要

移动自闭症风险评估(MARA)是一种新型的、通过电子方式管理的、包含7个问题的自闭症谱系障碍(ASD)筛查工具,用于对ASD高危人群进行分流。对年龄在16个月至17岁的儿童(N = 222)在发育行为儿科诊所首次就诊时进行了筛查。将MARA评分与临床诊断结果进行比较。参与者的年龄中位数为5.8岁,76.1%为男性,大多数参与者的智力/发育商数得分>85;69名参与者(31%)被临床诊断为ASD。MARA检测ASD的敏感性为89.9%[95%置信区间=82.7 - 97];特异性为79.7%[95%置信区间=73.4 - 86.1]。在高危临床环境中,MARA有望作为一种筛查工具,用于区分ASD与其他发育/行为障碍。

相似文献

1
Clinical Evaluation of a Novel and Mobile Autism Risk Assessment.一种新型移动自闭症风险评估的临床评估
J Autism Dev Disord. 2016 Jun;46(6):1953-1961. doi: 10.1007/s10803-016-2718-4.
2
Efficacy of the ADEC in Identifying Autism Spectrum Disorder in Clinically Referred Toddlers in the US.美国ADEC在识别临床转诊幼儿自闭症谱系障碍方面的有效性。
J Autism Dev Disord. 2015 Aug;45(8):2337-48. doi: 10.1007/s10803-015-2398-5.
3
Screening strategies for autism spectrum disorders in pediatric primary care.儿科初级保健中自闭症谱系障碍的筛查策略。
J Dev Behav Pediatr. 2008 Oct;29(5):345-50. doi: 10.1097/DBP.0b013e31818914cf.
4
Screening in toddlers and preschoolers at risk for autism spectrum disorder: Evaluating a novel mobile-health screening tool.对自闭症谱系障碍风险的幼儿和学龄前儿童进行筛查:评估一种新的移动健康筛查工具。
Autism Res. 2018 Jul;11(7):1038-1049. doi: 10.1002/aur.1959. Epub 2018 May 7.
5
Modeling clinical outcome of children with autistic spectrum disorders.对自闭症谱系障碍儿童的临床结果进行建模。
Pediatrics. 2005 Jul;116(1):117-22. doi: 10.1542/peds.2004-1118.
6
Detecting Developmental Delay and Autism Through Machine Learning Models Using Home Videos of Bangladeshi Children: Development and Validation Study.通过使用孟加拉国儿童家庭视频的机器学习模型检测发育迟缓与自闭症:开发与验证研究
J Med Internet Res. 2019 Apr 24;21(4):e13822. doi: 10.2196/13822.
7
Adherence to screening and referral guidelines for autism spectrum disorder in toddlers in pediatric primary care.在儿科初级保健中,坚持对幼儿自闭症谱系障碍进行筛查和转介的指导原则。
PLoS One. 2020 May 7;15(5):e0232335. doi: 10.1371/journal.pone.0232335. eCollection 2020.
8
The Autism Detection in Early Childhood Tool: Level 2 autism spectrum disorder screening in a NICU Follow-up program.婴幼儿自闭症早期检测工具:NICU 随访项目中的二级自闭症谱系障碍筛查。
Infant Behav Dev. 2021 Nov;65:101650. doi: 10.1016/j.infbeh.2021.101650. Epub 2021 Oct 13.
9
Evaluation of the Diagnostic Stability of the Early Autism Spectrum Disorder Phenotype in the General Population Starting at 12 Months.12 个月龄起于普通人群中早期自闭症谱系障碍表型的诊断稳定性评估。
JAMA Pediatr. 2019 Jun 1;173(6):578-587. doi: 10.1001/jamapediatrics.2019.0624.
10
Prevalence of Autism Spectrum Disorder in Children Referred for Diagnostic Autism Evaluation.因自闭症诊断评估而接受转诊的儿童中自闭症谱系障碍的患病率。
Clin Pediatr (Phila). 2015 Dec;54(14):1322-7. doi: 10.1177/0009922815592607. Epub 2015 Jun 29.

引用本文的文献

1
Enhancing Adult Autism Diagnostic Pathways: The Role of Clinical Triage in Efficient Service Provision.加强成人自闭症诊断途径:临床分诊在高效服务提供中的作用。
J Clin Med. 2025 Apr 24;14(9):2933. doi: 10.3390/jcm14092933.
2
Latest clinical frontiers related to autism diagnostic strategies.与自闭症诊断策略相关的最新临床前沿进展。
Cell Rep Med. 2025 Feb 18;6(2):101916. doi: 10.1016/j.xcrm.2024.101916. Epub 2025 Jan 28.
3
Creating a diagnostic assessment model for autism spectrum disorder by differentiating lexicogrammatical choices through machine learning.

本文引用的文献

1
A Review of Level 2 Parent-Report Instruments Used to Screen Children Aged 1.5-5 for Autism: A Meta-Analytic Update.用于筛查1.5至5岁儿童自闭症的二级家长报告工具综述:元分析更新
J Autism Dev Disord. 2015 Aug;45(8):2519-30. doi: 10.1007/s10803-015-2419-4.
2
Prevalence of autism spectrum disorder among children aged 8 years - autism and developmental disabilities monitoring network, 11 sites, United States, 2010.8 岁儿童自闭症谱系障碍患病率 - 自闭症和发育障碍监测网络,11 个地点,美国,2010 年。
MMWR Surveill Summ. 2014 Mar 28;63(2):1-21.
3
Autism detection in early childhood (ADEC): reliability and validity data for a Level 2 screening tool for autistic disorder.
通过机器学习区分词汇语法选择,创建自闭症谱系障碍的诊断评估模型。
PLoS One. 2024 Sep 27;19(9):e0311209. doi: 10.1371/journal.pone.0311209. eCollection 2024.
4
A machine learning model based on CHAT-23 for early screening of autism in Chinese children.一种基于CHAT-23的机器学习模型,用于中国儿童自闭症的早期筛查。
Front Pediatr. 2024 Sep 10;12:1400110. doi: 10.3389/fped.2024.1400110. eCollection 2024.
5
The Promises and Possibilities of Artificial Intelligence in the Delivery of Behavior Analytic Services.人工智能在行为分析服务提供中的前景与可能性。
Behav Anal Pract. 2023 Oct 11;17(1):123-136. doi: 10.1007/s40617-023-00864-3. eCollection 2024 Mar.
6
Digitally Diagnosing Multiple Developmental Delays Using Crowdsourcing Fused With Machine Learning: Protocol for a Human-in-the-Loop Machine Learning Study.利用众包与机器学习相结合的方法进行数字诊断多种发育迟缓:人在回路机器学习研究方案
JMIR Res Protoc. 2024 Feb 8;13:e52205. doi: 10.2196/52205.
7
Equivalence of the autism spectrum disorders diagnostics in children in telemedicine and face-to-face consultations: a literature review.儿童自闭症谱系障碍诊断在远程医疗和面对面咨询中的等效性:一项文献综述
Consort Psychiatr. 2023 Sep 29;4(3):55-64. doi: 10.17816/CP12496.
8
Longitudinal study of stool-associated microbial taxa in sibling pairs with and without autism spectrum disorder.对患有和未患有自闭症谱系障碍的同胞对中与粪便相关的微生物类群的纵向研究。
ISME Commun. 2021 Dec 18;1(1):80. doi: 10.1038/s43705-021-00080-6.
9
Machine Learning Differentiation of Autism Spectrum Sub-Classifications.机器学习在自闭症谱系分类中的应用
J Autism Dev Disord. 2024 Nov;54(11):4216-4231. doi: 10.1007/s10803-023-06121-4. Epub 2023 Sep 26.
10
A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism.自闭症神经精神表型的数据科学和机器学习综述及路线图。
Annu Rev Biomed Data Sci. 2023 Aug 10;6:211-228. doi: 10.1146/annurev-biodatasci-020722-125454. Epub 2023 May 3.
婴幼儿孤独症检测(ADEC):孤独症谱系障碍二级筛查工具的信度和效度数据。
Psychol Assess. 2014 Mar;26(1):215-26. doi: 10.1037/a0034472. Epub 2014 Feb 3.
4
Use of artificial intelligence to shorten the behavioral diagnosis of autism.利用人工智能缩短自闭症的行为诊断时间。
PLoS One. 2012;7(8):e43855. doi: 10.1371/journal.pone.0043855. Epub 2012 Aug 27.
5
How useful is the Social Communication Questionnaire in toddlers at risk of autism spectrum disorder?《社会沟通问卷》在自闭症谱系障碍风险婴幼儿中的应用价值如何?
J Child Psychol Psychiatry. 2010 Nov;51(11):1260-8. doi: 10.1111/j.1469-7610.2010.02246.x.
6
Screening accuracy of Level 2 autism spectrum disorder rating scales. A review of selected instruments.二级自闭症谱系障碍评定量表的筛查准确性。部分工具的综述。
Autism. 2010 Jul;14(4):263-84. doi: 10.1177/1362361309348071.
7
Randomized, controlled trial of an intervention for toddlers with autism: the Early Start Denver Model.自闭症幼儿干预的随机对照试验:早期丹佛模式。
Pediatrics. 2010 Jan;125(1):e17-23. doi: 10.1542/peds.2009-0958. Epub 2009 Nov 30.
8
Clinical assessment and management of toddlers with suspected autism spectrum disorder: insights from studies of high-risk infants.疑似自闭症谱系障碍幼儿的临床评估与管理:来自高危婴儿研究的见解
Pediatrics. 2009 May;123(5):1383-91. doi: 10.1542/peds.2008-1606.
9
Use of the Screening Tool for Autism in Two-Year-Olds (STAT) for children under 24 months: an exploratory study.针对24个月以下儿童使用两岁儿童自闭症筛查工具(STAT):一项探索性研究。
Autism. 2008 Sep;12(5):557-73. doi: 10.1177/1362361308096403.
10
Identification and evaluation of children with autism spectrum disorders.自闭症谱系障碍儿童的识别与评估。
Pediatrics. 2007 Nov;120(5):1183-215. doi: 10.1542/peds.2007-2361. Epub 2007 Oct 29.