研究论文:在MEDLINE中搜索临床预测规则

B. J. Ingui, M. Rogers
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引用次数: 71

摘要

目的:临床预测规则被认为是提高诊断、治疗和预后评估的临床判断的可能机制。尽管人们对它们的使用重新产生了兴趣,但不一致的术语使得计算机搜索系统难以对它们进行索引和检索。在文献中没有有效的方法来定位临床预测规则。本研究的目的是利用美国国家医学图书馆的MEDLINE数据库,推导并验证一个用于检索临床预测规则的最佳搜索过滤器。设计:进行对比性、回顾性分析。“金标准”是通过对1991年至1998年间选定的印刷期刊上的所有文章进行人工搜索而建立的,这些文章涵盖了临床预测规则的各个方面,如推导、验证和评估。通过分析用于索引每篇文章的文本词(标题和摘要中的单词)和医学主题标题(来自MeSH Thesaurus),从期刊7月至12月的文章(衍生集)中衍生出搜索过滤器。然后使用1月至6月的文章(验证集)评估这些过滤器在检索临床预测规则方面的准确性。测量:测量了几种不同搜索过滤器的敏感性、特异性、阳性预测值和阳性似然比。结果:过滤器“预测$或临床$或结果$或风险$”检索到98%的临床预测规则。四个过滤器,如“预测或验证或规则或测试的预测值”,其灵敏度和特异性都在90%以上。在验证集中,对正预测值和正似然比表现最好的过滤器是“predict$.ti”。美元和规则。”结论:找到了几种具有较高检索价值的筛选方法。根据搜索者的目标和时间限制,可以使用其中一个过滤器。
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Research Paper: Searching for Clinical Prediction Rules in MEDLINE
Objectives: Clinical prediction rules have been advocated as a possible mechanism to enhance clinical judgment in diagnostic, therapeutic, and prognostic assessment. Despite renewed interest in the their use, inconsistent terminology makes them difficult to index and retrieve by computerized search systems. No validated approaches to locating clinical prediction rules appear in the literature. The objective of this study was to derive and validate an optimal search filter for retrieving clinical prediction rules, using the National Library of Medicine's MEDLINE database. Design: A comparative, retrospective analysis was conducted. The "gold standard" was established by a manual search of all articles from select print journals for the years 1991 through 1998, which identified articles covering various aspects of clinical prediction rules such as derivation, validation, and evaluation. Search filters were derived, from the articles in the July through December issues of the journals (derivation set), by analyzing the textwords (words in the title and abstract) and the medical subject heading (from the MeSH Thesaurus) used to index each article. The accuracy of these filters in retrieving clinical prediction rules was then assessed using articles in the January through June issues (validation set). Measurements: The sensitivity, specificity, positive predictive value, and positive likelihood ratio of several different search filters were measured. Results: The filter "predict$ OR clinical$ OR outcome$ OR risk$" retrieved 98 percent of clinical prediction rules. Four filters, such as "predict$ OR validat$ OR rule$ OR predictive value of tests," had both sensitivity and specificity above 90 percent. The top-performing filter for positive predictive value and positive likelihood ratio in the validation set was "predict$.ti. AND rule$." Conclusions: Several filters with high retrieval value were found. Depending on the goals and time constraints of the searcher, one of these filters could be used.
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