由 ChatGPT 生成的圆排列图，用于识别 2022 年麻醉学作者文章的特征：文献计量分析。

BACKGROUND: The ChatGPT (Open AI, San Francisco, CA), denoted by the Chat Generative Pretrained Transformer, has been a hot topic for discussion over the past few months. A verification of whether the code for drawing circle packing charts (CPCs) with R can be generated by ChatGPT and used to identify characteristics of articles by anesthesiology authors is needed. This study aimed to provide insights into article characteristics in the field of anesthesiology and to highlight the potential of ChatGPT for data visualization techniques (e.g., CPCs) in bibliometric analysis. METHODS: A total of 23,012 articles were indexed in PubMed in 2022 by authors in the field of anesthesiology. The code for drawing CPCs with R was generated by ChatGPT and then modified by the authors to identify the characteristics of articles in 2 forms: 23,012 and 100 top-impact factors in journals (T100IF). Using CPCs and 3 other visualizations-network charts, impact beam plots, and Sankey diagrams-we were able to display article features commonly used in bibliometric analysis. The author-weighted scheme and absolute advantage coefficient were used to assess dominant entities, such as countries, institutes, authors, and themes (defined by PubMed and MeSH terms). RESULTS: Our findings indicate that: further modifications should be made to the code generated by ChatGPT for drawing CPCs in R; publications in the field of anesthesiology are dominated by China, followed by the United States and Japan; Capital Medical University (China) and Showa University Hospital (Japan) dominate research institutes in terms of publications and IF, respectively; and COVID-19 is the most frequently reported theme in T100IF, accounting for 29%. CONCLUSIONS: No such articles with CPCs regarding bibliometrics have ever been found in PubMed. The code for drawing CPCs with R can be generated by ChatGPT, but further modification is required for implementation in bibliometrics. CPCs should be used in future studies to identify the characteristics of articles in other areas of research rather than limiting them to anesthesiology, as we did in this study.

背景：ChatGPT（Open AI，旧金山，CA），即 Chat 生成预训练转换器，在过去几个月里一直是讨论的热点。需要验证 ChatGPT 是否可以生成用于识别麻醉学作者文章特征的绘制圆形包装图（CPC）的代码，并将其用于生物计量分析中的数据可视化技术（例如 CPC）。本研究旨在深入了解麻醉学领域的文章特征，并强调 ChatGPT 在数据可视化技术（例如 CPC）在生物计量分析中的潜力。

方法：2022 年，麻醉学领域的作者在 PubMed 中索引了 23012 篇文章。ChatGPT 生成了用于绘制 CPC 的 R 代码，然后由作者进行修改，以两种形式识别文章的特征：23012 篇文章和 100 个顶级影响因子期刊（T100IF）。使用 CPC 以及其他 3 种可视化方法-网络图表、影响梁图和 Sankey 图-我们能够显示在生物计量分析中常用的文章特征。使用作者加权方案和绝对优势系数评估主导实体，如国家、机构、作者和主题（由 PubMed 和 MeSH 术语定义）。

结果：我们的研究结果表明：需要对 ChatGPT 生成的用于在 R 中绘制 CPC 的代码进行进一步修改；麻醉学领域的出版物主要由中国主导，其次是美国和日本；首都医科大学（中国）和昭和大学医院（日本）在出版物和 IF 方面分别主导着研究机构；T100IF 中报道最多的主题是 COVID-19，占 29%。

结论：在 PubMed 中从未发现过有关生物计量学的 CPC 文章。可以由 ChatGPT 生成用于绘制 CPC 的 R 代码，但在生物计量学中实施需要进一步修改。未来的研究应该使用 CPC 来识别其他研究领域的文章特征，而不是像我们在这项研究中那样仅限于麻醉学。