Demystifying the Search Button: A Comprehensive PubMed Search Strategy for Performing an Exhaustive Literature Review
journal contributionposted on 17.02.2016 by L. McKeever, V. Nguyen, S. Peterson, S. Gomez-Perez, C. Braunschweig
Any type of content formally published in an academic journal, usually following a peer-review process.
A thorough review of the literature is the basis of all research and evidence based practice. A gold standard efficient and exhaustive search strategy is needed to assure all relevant citations have been captured and that the search performed is reproducible. The PubMed database is comprised of both the MEDLINE and nonMEDLINE databases. MEDLINE based search strategies are robust, but capture only 89% of the total available citations in PubMed. The remaining 11% include the most recent and possibly relevant citations, but are only searchable through less efficient techniques. An effective search strategy must employ both the MEDLINE and the non-MEDLINE portion of PubMed to ensure all studies have been identified. The robust MEDLINE search strategies are used for the MEDLINE portion of the search. Usage of the less robust strategies is then efficiently confined to search only the remaining 11% of PubMed citations which have not been indexed for MEDLINE. The current paper offers step-by-step instructions for building such a search exploring methods for the discovery of medical subject heading (MeSH) terms to search MEDLINE, text-based methods for exploring the non-MEDLINE database, information on the limitations of convenience algorithms such as the ‘related citations feature’, the strengths and pitfalls associated with commonly used filters, the proper usage of Boolean operators to organize a master search strategy and instructions for automating that search through ‘MyNCBI’ to receive search query updates by email as new citations become available. The ability to perform an effective search strategy comprises the foundation of any evidence-based discipline. Whether one is functioning in a clinical or research capacity, the assumption is that the expert suggestions offered arise from a complete review of the relevant literature. In systematic reviews and metaanalyses, this is taken a step further, necessitating that the methods of these exhaustive literature reviews be reproducible. The conveniences afforded by the basic PubMed search engine and algorithms, such as the ‘Related Citations’ feature provide powerful search tools, but take much of the control out of the hands of the user. Blind usage of these hands-off methods weakens the user’s ability to ensure a thorough, systematic, and reproducible search. A common solution to this problem is to limit the search strategy to include only the MEDLINE portion of the PubMed database, allowing for the usage of more robust, user-controlled search methods. Unfortunately, depending upon the journal indexed, MEDLINE is often as much as a year behind the current research, leaving many of the most recent and relevant research citations out of the search results. The strategies presented in this article offer a solution to this problem. The general landscape of PubMed has been previously described in a recent article by Lindsey and Olin. The objective of the current article is to augment that landscape by proposing a detailed method through which its many components may be employed and integrated into one highly efficient, reproducible, and exhaustive search strategy. True comprehension of this system provides the ability to create a master search for any topic. Furthermore, the many free tools provided by the National Center for Biotechnology Information (NCBI) and the National Library of Medicine (NLM) through PubMed make it possible to receive timely notifications of citations, directly relevant to any area of one’s field as they become available. Part I of this paper will discuss theories, methods, and key concepts for using Medical Subject Headings (MeSH) to search PubMed’s MEDLINE database, the less robust strategies necessary for searching the remaining nonMEDLINE database and methods for restricting the less robust search tools to search only the non-MEDLINE database. Part II will provide a step-by-step tutorial for implementing Part I as well as methods for automating a search strategy so the user may receive daily, weekly, or monthly ongoing updated results to their search query by email.