An exploding quantity of information, increasing time constraints, and the inadequacy of traditional sources of information underline the importance for clinicians to search efficiently for evidence-based and up-to-date medical information to support diagnostic, prognostic, and therapeutic decision-making processes . Clinical practice guidelines (CPGs) are "systematically developed statements to assist practitioner and patient decisions about appropriate health care for specific clinical circumstances" , and are becoming an increasingly familiar part of medical practice . Factors that make searches for CPGs problematic include incomplete indexing in bibliographic databases, as well as the difficulties clinicians encounter in selecting optimal search strategies [4, 5]. CPG-specific databases exist, such as the NGC (National Guideline Clearinghouse), which includes CPGs that meet defined inclusion criteria and have been published within the previous 5 years . However, so far no search engine is available that searches all guideline databases (e.g. NGC, NHS Clinical Knowledge Summaries , Canadian Medical Association Infobase – Clinical Practice Guidelines ).
The free-access Internet search engines SUMSearch and Google Scholar are widely used when searching for medical information. SUMSearch was developed by the University of Texas in 1999 [9, 10]. It searches the Internet for evidence-based medical information, scanning databases (MEDLINE, DARE, and NGC) as well as various high-impact medical journals . SUMSearch provides validated integrated search filters such as the diagnosis filter developed by Haynes et al . To automate searching, SUMSearch combines meta- and contingency searching. Meta-searching is designed to scan multiple databases and sites simultaneously, and returns one single retrieval document to the user. If too many retrievals are obtained, more restrictive searches (contingency searches) are conducted by activating additional filters. Conversely, if the number of retrievals is too small, SUMSearch adds more databases to the search . The retrievals in SUMSearch are presented in a box with categories arranged from narrative reviews with a broad perspective to publications that are more specific and more difficult to read [11, 14]. Within the categories, the search results are organised according to database, and are ranked predominantly by publication date in descending order.
Google Scholar is a search engine that was launched in November 2004 by Google Inc. It is available in the beta test-version, which is in continuous transition . Google Scholar is organised according to a so-called federated search : its web crawlers search, process, and index information in the World Wide Web, incorporating it into a single repository, and it refers to this repository to process a search. Google Scholar was developed to provide "a simple way to broadly search for scholarly literature" across many sources (e.g. peer-reviewed papers, books, academic publishers, and universities) . However, further details are not provided; for example, the sources or the search algorithms have not been disclosed, and the term "scholarly" has not been defined [18–22]. In contrast to SUMSearch, Google Scholar presents the search results in a ranked list, the retrievals sorted according to relevance, taking the number of citations into account . However, this system is not strictly applied and may be biased by the high number of citations of older records [19, 23]. In both Google Scholar and SUMSearch, the search strategy and search results cannot be saved.
Our analysis was motivated by the fact that we had previously not identified studies that compared search strategies for CPGs in SUMSearch and Google Scholar by means of diagnostic parameters. The model for our study was provided by the analysis methods introduced by the Hedges group to detect different types of studies in different databases [12, 24–31]. The aim of this study was to provide clinicians with useful search strategies to identify CPGs in SUMSearch and Google Scholar.