Department of Ophthalmology, Chi-Mei Medical Center, Tainan, Taiwan,Department of Optometry, Chung Hwa University of Medical Technology, Tainan, Taiwan,Department of Medical Research, Chi-Mei Medical Center, Tainan, Taiwan,Medical School, St. George's University of London, London, United Kingdom,Department of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan,Department of Physical Medicine and Rehabilitation, Chi-Mei Hospital Chiali, Tainan, Taiwan,Department of Physical Medicine and Rehabilitation, Chung San Medical University Hospital, Taichung, Taiwan.
Medicine (Baltimore). 2022 Mar 18;101(11). doi: 10.1097/MD.0000000000029029.
: Little systematic information has been collected about the nature and types of articles published in 2 journals by identifying the latent topics and analyzing the extracted research themes and sentiments using text mining and machine learning within the 2020 time frame. The goals of this study were to conduct a content analysis of articles published in 2 journals, describe the research type, identify possible gaps, and propose future agendas for readers.
: We downloaded 5610 abstracts in the journals of and the (JFMA) from the PubMed library in 2020. Sentiment analysis (ie, opinion mining using a natural language processing technique) was performed to determine whether the article abstract was positive or negative toward sentiment to help readers capture article characteristics from journals. Cluster analysis was used to identify article topics based on medical subject headings (MeSH terms) using social network analysis (SNA). Forest plots were applied to distinguish the similarities and differences in article mood and MeSH terms between these 2 journals. The statistic and index were used to evaluate the difference in proportions of MeSH terms in journals.
: The comparison of research topics between the 2 journals using the 737 cited articles was made and found that most authors are from mainland China and Taiwan in and respectively, similarity is supported by observing the abstract mood ( = 8.3, = 0, = .68; Z = 0.46, = .65), 2 journals are in a common cluster (named latent topic of patient and treatment) using SNA, and difference in overall effect was found by the odds ratios of MeSH terms ( = 185.5 = 89.8, < .001; = 5.93, < .001) and a greater proportion of COVID-19 articles in
: SNA and forest plots were provided to readers with deep insight into the relationships between journals in research topics using MeSH terms. The results of this research provide readers with a concept diagram for future submissions to a given journal.
The main approaches frequently used in Meta-analysis for drawing forest plots contributed to the following: 1. Comparing abstract mood in 2 journals, which is modern and innovative in the literature. 2. Extracting article topics from MeSH terms using SNA, 3. drawing visual representations by using SNA, choropleth map, and forest plots that can inspire other relevant research to replicate the approaches for the other 2 paired journals in comparison of differences in research topics in the future.
通过在 2020 年的时间范围内使用文本挖掘和机器学习来识别潜在主题并分析提取的研究主题和情感，很少有系统的信息被收集到这两个期刊发表的文章的性质和类型。本研究的目的是对发表在两个期刊上的文章进行内容分析，描述研究类型，识别可能存在的差距，并为读者提出未来的议程。
我们从 2020 年的 PubMed 库中下载了 5610 篇 和 （JFMA）期刊的摘要。情感分析（即使用自然语言处理技术的意见挖掘）被用来确定文章摘要的情感是积极的还是消极的，以帮助读者从期刊中捕捉文章的特征。基于医学主题词（MeSH 术语）的社会网络分析（SNA）用于聚类分析，以识别文章主题。森林图用于区分这两个期刊上文章情绪和 MeSH 术语的相似性和差异性。应用 统计量和 指数来评估期刊中 MeSH 术语的比例差异。
使用 737 篇引用文章对两个期刊的研究主题进行了比较，发现 和 期刊的大多数作者分别来自中国大陆和台湾，通过观察摘要情绪得到了支持（ = 8.3， = 0， =.68；Z = 0.46， =.65），使用 SNA 发现两个期刊处于一个共同的聚类（命名为患者和治疗的潜在主题），MeSH 术语的比值比（ = 185.5 = 89.8， <.001； = 5.93， <.001）和更多的 COVID-19 文章比例存在差异。
SNA 和森林图为读者提供了深入了解期刊在使用 MeSH 术语的研究主题之间关系的方法。本研究的结果为读者提供了一个未来向特定期刊投稿的概念图。
元分析中常用于绘制森林图的主要方法有：1. 比较两个期刊的摘要情绪，这在文献中是现代和创新的。2. 使用 SNA 从 MeSH 术语中提取文章主题。3. 通过使用 SNA、地理区域图和森林图进行可视化表示，为其他相关研究提供灵感，以便在未来比较其他两个配对期刊的研究主题差异时复制这些方法。
