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口腔白色病变:最新临床诊断决策树

Oral White Lesions: An Updated Clinical Diagnostic Decision Tree.

作者信息

Mortazavi Hamed, Safi Yaser, Baharvand Maryam, Jafari Soudeh, Anbari Fahimeh, Rahmani Somayeh

机构信息

Oral Medicine Department, School of Dentistry, Shahid Beheshti University of Medical Sciences, 1983969411 Tehran, Iran.

Oral and Maxillofacial Radiology Department, School of Dentistry, Shahid Beheshti University of Medical Sciences, 1983969411 Tehran, Iran.

出版信息

Dent J (Basel). 2019 Feb 7;7(1):15. doi: 10.3390/dj7010015.

Abstract

Diagnosis of oral white lesions might be quite challenging. This review article aimed to introduce a decision tree for oral white lesions according to their clinical features. General search engines and specialized databases including PubMed, PubMed Central, EBSCO, Science Direct, Scopus, Embase, and authenticated textbooks were used to find relevant topics by means of MeSH keywords such as "mouth disease", "oral keratosis", "oral leukokeratosis", and "oral leukoplakia". Related English-language articles published since 2000 to 2017, including reviews, meta-analyses, and original papers (randomized or nonrandomized clinical trials; prospective or retrospective cohort studies), case reports, and case series about oral diseases were appraised. Upon compilation of data, oral white lesions were categorized into two major groups according to their nature of development: Congenital or acquired lesions and four subgroups: Lesions which can be scraped off or not and lesions with the special pattern or not. In total, more than 20 entities were organized in the form of a decision tree in order to help clinicians establish a logical diagnosis by a stepwise progression method.

摘要

口腔白色病变的诊断可能颇具挑战性。这篇综述文章旨在根据口腔白色病变的临床特征介绍一种诊断决策树。通过使用通用搜索引擎以及包括PubMed、PubMed Central、EBSCO、Science Direct、Scopus、Embase在内的专业数据库和权威教科书,借助“口腔疾病”、“口腔角化病”、“口腔黏膜下纤维化”和“口腔白斑病”等医学主题词(MeSH)来查找相关主题。对2000年至2017年期间发表的相关英文文章进行了评估,这些文章包括综述、荟萃分析和原创论文(随机或非随机临床试验;前瞻性或回顾性队列研究)、病例报告以及关于口腔疾病的病例系列。在汇总数据后,根据口腔白色病变的发展性质将其分为两大类:先天性或后天性病变,以及四个亚组:可刮除或不可刮除的病变、有无特殊形态的病变。总共以决策树的形式整理了20多个实体,以帮助临床医生通过逐步推进的方法进行逻辑诊断。

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