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计算机可解释的指南:电子工具,用于增强甲状腺结节临床实践指南和风险分层工具的实用性。

Computer-interpretable guidelines: electronic tools to enhance the utility of thyroid nodule clinical practice guidelines and risk stratification tools.

机构信息

Atrius Health, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States.

Deontics, London, United Kingdom.

出版信息

Front Endocrinol (Lausanne). 2023 Aug 15;14:1228834. doi: 10.3389/fendo.2023.1228834. eCollection 2023.

DOI:10.3389/fendo.2023.1228834
PMID:37654563
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10465787/
Abstract

Clinicians seeking guidance for evaluating and managing thyroid nodules currently have several resources. The principal ones are narrative clinical guidelines and clinical risk calculators. This paper will review the strengths and weaknesses of both. The paper will introduce a concept of computer interpretable guideline, a novel way of transforming narrative guidelines in to a clinical decision support tool that can provide patient specific recommendations at the point of care. The paper then describes an experience of developing an interactive web based computer interpretable guideline for thyroid nodule management, called Thyroid Nodule Management App (TNAPP). The advantages of this approach and the potential barriers for widespread adaptation are discussed.

摘要

临床医生在评估和管理甲状腺结节时,目前有多种资源可供参考。其中主要的是叙述性临床指南和临床风险计算器。本文将对这两者的优缺点进行综述。本文将介绍计算机可解释性指南的概念,这是一种将叙述性指南转化为临床决策支持工具的新方法,可以在护理点为患者提供具体的建议。然后,本文描述了开发一种名为甲状腺结节管理应用程序(TNAPP)的用于甲状腺结节管理的交互式基于网络的计算机可解释性指南的经验。讨论了这种方法的优势和广泛应用的潜在障碍。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bfbc/10465787/79f65dfe14d5/fendo-14-1228834-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bfbc/10465787/79f65dfe14d5/fendo-14-1228834-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bfbc/10465787/79f65dfe14d5/fendo-14-1228834-g001.jpg

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本文引用的文献

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Deep learning for classification of thyroid nodules on ultrasound: validation on an independent dataset.深度学习在超声甲状腺结节分类中的应用:独立数据集的验证。
Clin Imaging. 2023 Jul;99:60-66. doi: 10.1016/j.clinimag.2023.04.010. Epub 2023 Apr 24.
2
The TNAPP web-based algorithm improves thyroid nodule management in clinical practice: A retrospective validation study.基于网络的 TNAPP 算法改善了临床实践中的甲状腺结节管理:一项回顾性验证研究。
Front Endocrinol (Lausanne). 2023 Jan 27;13:1080159. doi: 10.3389/fendo.2022.1080159. eCollection 2022.
3
Thyroid nodules: Global, economic, and personal burdens.
甲状腺结节:全球性、经济性和个人负担。
Front Endocrinol (Lausanne). 2023 Jan 23;14:1113977. doi: 10.3389/fendo.2023.1113977. eCollection 2023.
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Validating and Comparing C-TIRADS, K-TIRADS and ACR-TIRADS in Stratifying the Malignancy Risk of Thyroid Nodules.验证和比较 C-TIRADS、K-TIRADS 和 ACR-TIRADS 在甲状腺结节恶性风险分层中的作用。
Front Endocrinol (Lausanne). 2022 Jun 17;13:899575. doi: 10.3389/fendo.2022.899575. eCollection 2022.
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American Association of Clinical Endocrinology And Associazione Medici Endocrinologi Thyroid Nodule Algorithmic Tool.美国临床内分泌学会和意大利内分泌学会甲状腺结节算法工具。
Endocr Pract. 2021 Jul;27(7):649-660. doi: 10.1016/j.eprac.2021.04.007. Epub 2021 Jun 3.
6
Development and Internal Validation of a Predictive Model for Individual Cancer Risk Assessment for Thyroid Nodules.甲状腺结节个体癌症风险评估预测模型的建立与内部验证
Endocr Pract. 2020 Oct;26(10):1077-1084. doi: 10.4158/EP-2020-0004.
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NPJ Digit Med. 2020 Feb 6;3:17. doi: 10.1038/s41746-020-0221-y. eCollection 2020.
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