title: A simple, fast, easy method to identify the evidence base in pain-relief research: validation of a computer search strategy used alone to identify quality randomized controlled trials.
abstract: 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: *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.
authors.forename: Tony K F
authors.initials: TKF
authors.lastname: Chow
authors.forename: Elean
authors.initials: E
authors.lastname: To
authors.forename: Colin S
authors.initials: CS
authors.lastname: Goodchild
authors.forename: John J
authors.initials: JJ
authors.lastname: McNeil
mesh_terms: D000698:Analgesia / Q000706:statistics & numerical data*; D019317:Evidence-Based Medicine / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods* / Q000706:statistics & numerical data; D020407:Internet / Q000706:statistics & numerical data; D059408:Pain Management; D010147:Pain Measurement / Q000379:methods / Q000706:statistics & numerical data; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*