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加权识别关键词网络技术:一种用于系统评价中识别关键词的结构化方法。

Weightage Identified Network of Keywords Technique: A Structured Approach in Identifying Keywords for Systematic Reviews.

作者信息

Sivakumar Sasidharan, Sivakumar Gowardhan

机构信息

Indian Council of Medical Research, New Delhi, India.

Department of Maxillofacial Prosthodontics, Ragas Dental College and Hospital, Chennai, India.

出版信息

Healthc Inform Res. 2025 Jan;31(1):48-56. doi: 10.4258/hir.2025.31.1.48. Epub 2025 Jan 31.

Abstract

OBJECTIVES

The objective of this study was to develop the weightage identified network of keywords (WINK) technique for selecting and utilizing keywords to perform systematic reviews more efficiently. This technique aims to improve the thoroughness and precision of evidence synthesis by employing a more rigorous approach to keyword selection.

METHODS

The WINK methodology involves generating network visualization charts to analyze the interconnections among keywords within a specific domain. This process integrates both computational analysis and subject expert insights to enhance the accuracy and relevance of the findings. In the example considered, the networking strength between the contexts of environmental pollutants with endocrine function as Q1 and systemic health with oral health-related terms as Q2 was examined, and keywords with limited networking strength were excluded. Utilizing the Medical Subject Headings (MeSH) terms identified from the WINK technique, a search string was built and compared to an initial search with fewer keywords.

RESULTS

The application of the WINK technique in building the search string yielded 69.81% and 26.23% more articles for Q1 and Q2, respectively, compared to conventional approaches. This significant increase demonstrates the technique's effectiveness in identifying relevant studies and ensuring comprehensive evidence synthesis.

CONCLUSIONS

By prioritizing keywords with higher weightage and utilizing network visualization charts, the WINK technique ensures comprehensive evidence synthesis and enhances accuracy in systematic reviews. Its effectiveness in identifying relevant studies marks a significant advancement in systematic review methodology, offering a more robust and efficient approach to keyword selection.

摘要

目的

本研究的目的是开发关键词加权识别网络(WINK)技术,以便更高效地选择和使用关键词来进行系统评价。该技术旨在通过采用更严格的关键词选择方法来提高证据综合的全面性和精确性。

方法

WINK方法包括生成网络可视化图表,以分析特定领域内关键词之间的相互联系。这一过程整合了计算分析和主题专家的见解,以提高研究结果的准确性和相关性。在考虑的示例中,研究了环境污染物与内分泌功能(作为Q1)以及全身健康与口腔健康相关术语(作为Q2)之间的网络强度,并排除了网络强度有限的关键词。利用从WINK技术中识别出的医学主题词(MeSH)术语构建搜索字符串,并与使用较少关键词的初始搜索进行比较。

结果

与传统方法相比,WINK技术在构建搜索字符串时,分别为Q1和Q2生成的文章数量多出69.81%和26.23%。这一显著增加证明了该技术在识别相关研究和确保全面证据综合方面的有效性。

结论

通过优先考虑权重较高的关键词并使用网络可视化图表,WINK技术确保了全面的证据综合,并提高了系统评价的准确性。其在识别相关研究方面的有效性标志着系统评价方法的重大进步,为关键词选择提供了一种更强大、更有效的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2a6/11854614/6db4b01e4d2c/hir-2025-31-1-48f1.jpg

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