Chow Tony K F, To Elean, Goodchild Colin S, McNeil John J
*Department of Anaesthesia, Monash University, Monash Medical Centre, Clayton, Victoria, Australia; †Templestowe District Medical Centre, Templestowe, Victoria, Australia; and ‡Department of Epidemiology and Preventive Medicine, Monash University, Alfred Hospital, Prahran, Victoria, Australia.
Anesth Analg. 2004 Jun;98(6):1557-1565. doi: 10.1213/01.ANE.0000114071.78448.2D.
Clinicians need a simple, fast, reliable, and inexpensive way of identifying the evidence base relevant to their clinical practice. It is often believed that the only way to identify all relevant evidence is to perform hand-searches of the literature to supplement computer searches; this is complex and labor intensive. However, most of quality randomized controlled trials cited in systematic reviews in pain medicine are listed in computer databases. We performed two studies to investigate the efficiency-in terms of sensitivity, specificity, and precision-of three computer search strategies: Optimally Sensitive Search Strategy, which is used by the Cochrane Collaboration; RCT.pt, a standard MEDLINE strategy; and DBRCT.af, which is a new single-line computer algorithm based on the assumption that double-blinded, randomized controlled trials would be indexed with "double-blind," "random," or variations of these terms in MEDLINE and EMBASE. DBRCT.af was found to be highly sensitive (97%) in identifying quality randomized controlled trials in pain medicine. The precision (ratio of randomized controlled trials to the number of nonrandomized trials identified) was 82%, and the specificity in excluding the nonrandomized controlled trials was 98%. We conclude that clinicians can now use DBRCT.af to update and conduct de novo systematic reviews in pain-relief research.
Quality evidence about what is good clinical practice in pain treatment is buried in the medical literature among large quantities of other information. This article describes how any clinician with access to the Internet can identify those quality studies reliably, quickly, and inexpensively.
临床医生需要一种简单、快速、可靠且廉价的方法来识别与其临床实践相关的证据基础。人们通常认为,识别所有相关证据的唯一方法是对手工检索文献以补充计算机检索;这既复杂又耗费人力。然而,疼痛医学系统评价中引用的大多数高质量随机对照试验都列在计算机数据库中。我们进行了两项研究,以从敏感性、特异性和精确性方面调查三种计算机检索策略的效率:Cochrane协作网使用的最佳敏感检索策略;RCT.pt,一种标准的医学主题词表(MEDLINE)策略;以及DBRCT.af,这是一种基于双盲随机对照试验会在医学主题词表(MEDLINE)和荷兰医学文摘数据库(EMBASE)中以“双盲”“随机”或这些术语的变体进行索引这一假设的新型单行计算机算法。结果发现,DBRCT.af在识别疼痛医学中的高质量随机对照试验方面具有高度敏感性(97%)。精确性(随机对照试验与所识别的非随机试验数量之比)为82%,排除非随机对照试验的特异性为98%。我们得出结论,临床医生现在可以使用DBRCT.af来更新并开展疼痛缓解研究的全新系统评价。
关于疼痛治疗中良好临床实践的高质量证据淹没在医学文献中的大量其他信息之中。本文描述了任何能够访问互联网的临床医生如何可靠、快速且廉价地识别那些高质量研究。