Novel Mutation in KCNQ2 Causing Ohtahara Syndrome

Medical Database Search Choon Shil Lee Department of Library&Information Science, Sookmyung Women's University, Seoul, Korea. cslee@sookmyung.ac.kr Abstract It is essential to search medical information precisely and efficiently in every aspect of medical practices and research activities. The growth of the medical literature has been tremendous in recent years, as exemplified by the annual growth of 710,000 records in MEDLINE in 2009, thus increasing the complexities of literature searching. Yet database search environments are changing toward very user-friendly ways facilitated by various hypertext linking capabilities such as "LinkOuts" to full texts and "reference linkings" among articles using Digital Object Identifiers (DOIs). Once a direct search of a keyword is initiated, a searcher can continue searching endlessly and seamlessly by simply clicking various links provided in the records retrieved. Search behaviors of researchers are changing accordingly, avoiding any complex or advanced searches. The basics of database search methods are described in this paper. A brief overview of major medical databases is given by database type to illustrate the differences in the information retrievable from such databases: MEDLINE/PubMed and KoreaMed are abstract databases; SCI/Web of Science, SCOPUS and KoMCI are citation indexes; and PubMed Central and Synapse are full text databases. Some of the advanced search features of each database are also noted: searches using MeSH terms in PubMed and KoreaMed; differences in the "related documents" algorithms of PubMed and SCI; citation analysis using "analyze results" in Web of Science and SCOPUS; and citation tracking in Synapse and PubMed. The Journal of the Korean Medical Association (JKMA) records are used for the illustration of such features. Key Words: Google, Google Scholar, KoreaMed, PubMed, PubMed Central, SCI, SCOPUS  

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