Suppr超能文献

耳鼻喉科学领域公众搜索行为趋势:二十年分析

Trends in Public Search Behavior for Otorhinolaryngology: A Two-Decade Analysis.

作者信息

Sezen Göktaş Seda, Ay Levent, Balaban Furkan, Arslan Hande

机构信息

University of Health Sciences Türkiye, Samsun Training and Research Hospital, Department of Otorhinolaryngology, Samsun, Türkiye.

Samsun University Faculty of Medicine, Department of Otorhinolaryngology, Samsun, Türkiye.

出版信息

Turk Arch Otorhinolaryngol. 2025 Sep 26;63(3):133-142. doi: 10.4274/tao.2025.2024-12-13. Epub 2025 Sep 24.

Abstract

OBJECTIVE

Google Trends provides data on searches made on Google from a specific region in a specific period. The aim of this study is to determine the focus of interest in otorhinolaryngology in Türkiye between 2004-2024 using this method.

METHODS

Otorhinolaryngology was studied in five subbranches; namely, otology, rhinology, laryngology, head and neck surgery, and facial plastics. The 70 most searched terms in the last 20 years related to these subbranches were determined. The change in the search rates of the terms belonging to each subbranch and the change in the search percentages of the subbranches compared to the total number were determined and evaluated over the years.

RESULTS

In all terms examined, significant increases were observed in general since 2004. However, decrease was observed in all terms, except a few, in 2020-2021, i.e., during the pandemic. In the comparison between subbranches, in the last few years, the lowest search rate was seen in laryngology with 16.86%, and the highest search rate was seen in otology with 24.06%.

CONCLUSION

Knowing the topics where interest is clustered can be used to guide future medical practices and scientific research.

摘要

目的

谷歌趋势提供特定时期内特定地区在谷歌上的搜索数据。本研究的目的是使用该方法确定2004年至2024年土耳其耳鼻喉科的关注焦点。

方法

对耳鼻喉科的五个分支进行了研究;即耳科学、鼻科学、喉科学、头颈外科和面部整形。确定了过去20年中与这些分支相关的70个搜索量最高的术语。确定并评估了各分支术语搜索率的变化以及各分支搜索百分比相对于总数的变化情况。

结果

在所有检查的术语中,自2004年以来总体上观察到显著增加。然而,在2020年至2021年,即疫情期间,除少数术语外,所有术语的搜索量均下降。在各分支之间的比较中,在过去几年中,喉科学的搜索率最低,为16.86%,耳科学的搜索率最高,为24.06%。

结论

了解兴趣集中的主题可用于指导未来的医疗实践和科学研究。

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