Shaikh Nader, Badgett Robert G, Pi Mina, Wilczynski Nancy L, McKibbon K Ann, Ketchum Andrea M, Haynes R Brian
University of Pittsburgh School of Medicine, General Academic Pediatrics, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, PA 15224, USA.
J Med Internet Res. 2011 Oct 19;13(4):e82. doi: 10.2196/jmir.1826.
Efficiently finding clinical examination studies--studies that quantify the value of symptoms and signs in the diagnosis of disease-is becoming increasingly difficult. Filters developed to retrieve studies of diagnosis from Medline lack specificity because they also retrieve large numbers of studies on the diagnostic value of imaging and laboratory tests.
The objective was to develop filters for retrieving clinical examination studies from Medline.
We developed filters in a training dataset and validated them in a testing database. We created the training database by hand searching 161 journals (n = 52,636 studies). We evaluated the recall and precision of 65 candidate single-term filters in identifying studies that reported the sensitivity and specificity of symptoms or signs in the training database. To identify best combinations of these search terms, we used recursive partitioning. The best-performing filters in the training database as well as 13 previously developed filters were evaluated in a testing database (n = 431,120 studies). We also examined the impact of examining reference lists of included articles on recall.
In the training database, the single-term filters with the highest recall (95%) and the highest precision (8.4%) were diagnosis[subheading] and "medical history taking"[MeSH], respectively. The multiple-term filter developed using recursive partitioning (the RP filter) had a recall of 100% and a precision of 89% in the training database. In the testing database, the Haynes-2004-Sensitive filter (recall 98%, precision 0.13%) and the RP filter (recall 89%, precision 0.52%) showed the best performance. The recall of these two filters increased to 99% and 94% respectively with review of the reference lists of the included articles.
Recursive partitioning appears to be a useful method of developing search filters. The empirical search filters proposed here can assist in the retrieval of clinical examination studies from Medline; however, because of the low precision of the search strategies, retrieving relevant studies remains challenging. Improving precision may require systematic changes in the tagging of articles by the National Library of Medicine.
有效地查找临床检查研究——即量化症状和体征在疾病诊断中的价值的研究——正变得越来越困难。为从医学数据库（Medline）中检索诊断研究而开发的筛选器缺乏特异性，因为它们还检索了大量关于影像学和实验室检查诊断价值的研究。
目的是开发用于从医学数据库（Medline）中检索临床检查研究的筛选器。
我们在一个训练数据集中开发筛选器，并在一个测试数据库中对其进行验证。我们通过手工检索161种期刊（共52636项研究）创建了训练数据库。我们评估了65个候选单术语筛选器在识别训练数据库中报告症状或体征敏感性和特异性的研究时的召回率和精确率。为了确定这些搜索词的最佳组合，我们使用了递归划分法。在一个测试数据库（共431120项研究）中评估了训练数据库中表现最佳的筛选器以及13个先前开发的筛选器。我们还研究了查阅纳入文章的参考文献列表对召回率的影响。
在训练数据库中，召回率最高（95%）和精确率最高（8.4%）的单术语筛选器分别是“诊断[副标题]”和“病史采集”[医学主题词（MeSH）]。使用递归划分法开发的多术语筛选器（RP筛选器）在训练数据库中的召回率为100%，精确率为89%。在测试数据库中，海恩斯2004敏感筛选器（召回率98%，精确率0.13%）和RP筛选器（召回率89%，精确率0.52%）表现最佳。查阅纳入文章的参考文献列表后，这两个筛选器的召回率分别提高到99%和94%。
递归划分法似乎是开发搜索筛选器的一种有用方法。这里提出的经验性搜索筛选器有助于从医学数据库（Medline）中检索临床检查研究；然而，由于搜索策略的精确率较低，检索相关研究仍然具有挑战性。提高精确率可能需要美国国立医学图书馆对文章标注方式进行系统性改变。
