Novel Citation-Based Literature Search Method: Application to Meta-analyses

基于引文的新颖文献检索方法：在荟萃分析中的应用

• 批准号: 9316711
• 负责人: A Cecile J W Janssens
• 金额: $ 35.1万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-15 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9316711
• 关键词: Area; Biomedical Research; Centers for Disease Control and Prevention (U.S.); Characteristics; Clinical Medicine; Clinical effectiveness; Collaborations; Communities; Complement; Consensus; Data; Databases; Eligibility Determination; Evidence Based Medicine; Foundations; Frequencies; Goals; Gold; Heterogeneity; Informatics; Internet; Intuition; Literature; Meta-Analysis; Methodology; Methods; Observational Study; Paper; Performance; Phase; Pilot Projects; Preventive Intervention; Process; Publications; Publishing; Regression Analysis; Research; Sample Size; Science; Series; Statistical Methods; Therapeutic Intervention; Time; Writing; base; density; design; disorder prevention; improved; inclusion criteria; novel; prospective; screening; study characteristics; symposium; systematic review

PROJECT SUMMARY/ABSTRACT The inefficiency of literature searches is an old informatics problem. There has been much progress in facilitating literature searches, such as the registration of trials and observational studies, the proposal of optimal search strategies for various fields, and the proposal of search filters, but the efficiency of literature searches has not markedly improved. The goal of this project is to strengthen the scientific basis of the CoCites method, a novel citation-based search method for scientific literature that we recently developed. The method aims to find articles that are similar to one or more `known' articles in two consecutive searches: 1) finding articles that are cited together with the known articles by ranking their co-citation frequencies; and 2) finding backward and forward citations by ranking their citation frequencies. Articles with frequencies above the selection thresholds are screened for similarity. In two pilot studies, where we aimed to reproduce the literature searches of 52 published meta-analyses, we showed that articles included in each meta-analysis ranked high on the list of (co-)citation frequencies. The CoCites method was more efficient than keyword searches and able to retrieve 80% of the studies included in the meta-analyses. The proposed project aims to refine the CoCites method and investigates its application in various fields. Similar as in the pilot studies, we will reproduce the literature searches of published meta-analyses, which allows to calculate the sensitivity and efficiency of the method. To investigate the method in relation to citation practices and study characteristics, we will create a database in which we document the characteristics of 250 published meta-analyses as well as characteristics of each of their included studies. The cocitations will be downloaded from Web of Science. We will write a macro in Microsoft Excel to automatically process the downloaded files and produce the co-citation and citation rankings. We will apply the method to all or selections of the 250 meta-analyses, where we investigate the sensitivity and efficiency while: varying the selection threshold of the first search (Aim 1); adding a third search of co-citations, direct citations or both (Aim 2); and varying the selection and number of `known' articles (Aim 4). We will investigate the impact of network and study characteristics on the sensitivity and efficiency using regression analysis (Aim 5). To investigate whether the results of the meta-analyses change when studies are not found, we repeat the meta-analyses excluding the `missed' studies (Aim 3). We will apply the method to meta-analyses in five fields that differ in citation practice (Aim 5). Finally, we will prospectively apply the method to five then-ongoing systematic reviews conducted at the Community Guide of the Centers for Disease Control and Prevention.

项目总结/摘要 文献检索效率低下是一个古老的信息学问题。在以下方面取得了很大进展： 促进文献检索，如试验和观察性研究的登记， 各领域的最佳搜索策略，以及搜索过滤器的建议，但文献的效率 搜索没有明显改善。该项目的目标是加强科学基础， CoCites方法，我们最近开发的一种新的基于引文的科学文献检索方法。的 方法的目的是在两次连续搜索中找到与一个或多个“已知”文章相似的文章：1） 通过对它们的共引频率进行排名来找到与已知文章一起被引用的文章;以及2） 通过对引用频率进行排名来查找向后和向前引用。频率高于 针对相似性筛选选择阈值。在两项试点研究中，我们旨在复制文献， 通过对52项已发表的荟萃分析的检索，我们发现，每项荟萃分析中包含的文章排名较高， 在（共同）引用频率列表中。CoCites方法比关键字搜索更有效， 检索纳入荟萃分析的80%的研究。拟议项目旨在完善CoCites 方法，并探讨其在各个领域的应用。与试点研究类似，我们将重现 对已发表的荟萃分析进行文献检索，以计算 法为了调查与引文实践和研究特点有关的方法，我们将创建一个 数据库中，我们记录了250个已发表的荟萃分析的特征以及 每一项研究的数据。引文可从Web of Science下载。我们将编写一个 Microsoft Excel中的一个宏，用于自动处理下载的文件并生成共同引文和引文 排名我们将把该方法应用于250项荟萃分析的全部或部分，在这些荟萃分析中，我们调查了 灵敏度和效率，同时：改变第一次搜索的选择阈值（目标1）;添加第三次搜索 共同引用、直接引用或两者兼而有之（目标2）;以及改变“已知”文章的选择和数量（目标 4）。我们将调查网络的影响，并研究使用的灵敏度和效率的特点 回归分析（目标5）。为了调查当研究被纳入时，荟萃分析的结果是否会发生变化， 如果没有发现，我们重复荟萃分析，排除“遗漏”的研究（目标3）。我们将把这个方法应用到 在引文实践中存在差异的五个领域进行荟萃分析（目标5）。最后，我们将前瞻性地应用 方法，以五个当时正在进行的系统性审查，在社区指南的疾病中心 控制和预防。