Suppr超能文献

国家神经心理学网络的原理和设计。

Rationale and Design of the National Neuropsychology Network.

机构信息

Department of Neurology, Emory University School of Medicine, Atlanta, GA30329, USA.

Department of Pediatrics, Emory University School of Medicine, Atlanta, GA30322, USA.

出版信息

J Int Neuropsychol Soc. 2022 Jan;28(1):1-11. doi: 10.1017/S1355617721000199. Epub 2021 Mar 4.

Abstract

OBJECTIVE

The National Neuropsychology Network (NNN) is a multicenter clinical research initiative funded by the National Institute of Mental Health (NIMH; R01 MH118514) to facilitate neuropsychology's transition to contemporary psychometric assessment methods with resultant improvement in test validation and assessment efficiency.

METHOD

The NNN includes four clinical research sites (Emory University; Medical College of Wisconsin; University of California, Los Angeles (UCLA); University of Florida) and Pearson Clinical Assessment. Pearson Q-interactive (Q-i) is used for data capture for Pearson published tests; web-based data capture tools programmed by UCLA, which serves as the Coordinating Center, are employed for remaining measures.

RESULTS

NNN is acquiring item-level data from 500-10,000 patients across 47 widely used Neuropsychology (NP) tests and sharing these data via the NIMH Data Archive. Modern psychometric methods (e.g., item response theory) will specify the constructs measured by different tests and determine their positive/negative predictive power regarding diagnostic outcomes and relationships to other clinical, historical, and demographic factors. The Structured History Protocol for NP (SHiP-NP) helps standardize acquisition of relevant history and self-report data.

CONCLUSIONS

NNN is a proof-of-principle collaboration: by addressing logistical challenges, NNN aims to engage other clinics to create a national and ultimately an international network. The mature NNN will provide mechanisms for data aggregation enabling shared analysis and collaborative research. NNN promises ultimately to enable robust diagnostic inferences about neuropsychological test patterns and to promote the validation of novel adaptive assessment strategies that will be more efficient, more precise, and more sensitive to clinical contexts and individual/cultural differences.

摘要

目的

国家神经心理学网络(NNN)是一个由美国国立精神卫生研究所(NIMH;R01 MH118514 资助的多中心临床研究计划,旨在促进神经心理学向当代心理计量评估方法的转变,从而提高测试验证和评估效率。

方法

NNN 包括四个临床研究地点(埃默里大学;威斯康星医学院;加利福尼亚大学洛杉矶分校(UCLA);佛罗里达大学)和培生临床评估。培生 Q-interactive(Q-i)用于培生出版测试的数据采集;由担任协调中心的 UCLA 编程的基于网络的数据采集工具用于其余的测量。

结果

NNN 正在从 47 种广泛使用的神经心理学(NP)测试中获取 500-10000 名患者的项目级数据,并通过 NIMH 数据档案共享这些数据。现代心理计量学方法(例如,项目反应理论)将指定不同测试所测量的结构,并确定它们对诊断结果的阳性/阴性预测能力以及与其他临床、历史和人口统计学因素的关系。神经心理学结构化病史协议(SHiP-NP)有助于标准化获取相关病史和自我报告数据。

结论

NNN 是一个原理验证的合作:通过解决后勤挑战,NNN 旨在吸引其他诊所加入,创建一个全国性的,最终是国际性的网络。成熟的 NNN 将提供数据聚合机制,实现共享分析和协作研究。NNN 有望最终能够对神经心理学测试模式进行稳健的诊断推断,并促进新的自适应评估策略的验证,这些策略将更高效、更精确、更能适应临床环境和个体/文化差异。

相似文献

1
Rationale and Design of the National Neuropsychology Network.国家神经心理学网络的原理和设计。
J Int Neuropsychol Soc. 2022 Jan;28(1):1-11. doi: 10.1017/S1355617721000199. Epub 2021 Mar 4.
2
Neuropsychological tests of the future: How do we get there from here?未来的神经心理学测试:我们如何从这里到达那里?
Clin Neuropsychol. 2019 Feb;33(2):220-245. doi: 10.1080/13854046.2018.1521993. Epub 2018 Nov 13.
3
The future of Cochrane Neonatal.考克兰新生儿协作网的未来。
Early Hum Dev. 2020 Nov;150:105191. doi: 10.1016/j.earlhumdev.2020.105191. Epub 2020 Sep 12.
7
Measurement in cross-cultural neuropsychology.跨文化神经心理学中的测量
Neuropsychol Rev. 2008 Sep;18(3):184-93. doi: 10.1007/s11065-008-9067-9. Epub 2008 Sep 24.
8
Neuropsychology in Japan: history, current challenges, and future prospects.日本的神经心理学:历史、当前挑战及未来前景。
Clin Neuropsychol. 2016 Nov;30(8):1278-1295. doi: 10.1080/13854046.2016.1204012. Epub 2016 Aug 10.
9
Neuropsychology 3.0: evidence-based science and practice.神经心理学 3.0:基于证据的科学与实践。
J Int Neuropsychol Soc. 2011 Jan;17(1):7-13. doi: 10.1017/S1355617710001396. Epub 2010 Nov 19.

引用本文的文献

本文引用的文献

5
The role of RDoC in future classification of mental disorders
.RDoC 在未来精神障碍分类中的作用
Dialogues Clin Neurosci. 2020 Mar;22(1):81-85. doi: 10.31887/DCNS.2020.22.1/bcuthbert.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验