MEDLINE: A Guide to Effective Searching in PubMed and Other InterfacesAshbury Press, 2006 M02 1 - 136 páginas "....a well-written, quick read perfect for medical librarianship students, physicians, and researchers or anyone interested in improving their MEDLINE searching abilities." -- Journal of the Medical Library Association This concise and clearly written book will make your PubMed searches more productive. This completely revised second edition of Brian Katcher's MEDLINE: a guide to effective searching in PubMed and other interfaces promotes the cultivation of an informed and thoughtful approach to searching in PubMed/MEDLINE and other interfaces to MEDLINE. MEDLINE, the National Library of Medicine's on-line bibliographic database, is the premiere index to the world's biomedical literature. It is the primary component of PubMed. MEDLINE is exquisitely organized: each journal article is manually indexed under an average of a dozen Medical Subject Headings (MeSH Terms), one or more publication types, and more. An understanding of this organization is essential to effective searching. Any health professional, health sciences student, or researcher will benefit from reading this book. It explains the basics of formulating searches, shows how to put the main indexing elements in MEDLINE to best use, illustrates the importance of Medical Subject Headings (MeSH), provides guidance for framing questions, and backs everything up with practical examples. MEDLINE: a guide to effective searching in PubMed and other interfaces is an essential resource for those concerned with evidence-based medicine and those engaged in biomedical research. Medical librarians and teachers of medical informatics will find this book to be useful in promoting the careful use of PubMed/MEDLINE. Sometimes simply reading a linear narrative--even on a screen--is a good way to learn. In addition, PubMed offers excellent on-line tutorials. |
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... mapped to journal and author names. The current implementation of PubMed contains an array of additional powerful features that are easy to use, but users need to know how they work and when they might be helpful. In the early years ...
... mapped as [ MeSH Terms ] OR [ Text Words ] , which greatly increases recall ( see pages 33-34 ) . Text Words and the Recall / Precision Dilemma The expanded recall in the above search strategy was accompa- nied by a lack of precision ...
... mapped to MeSH vocabulary , if a match can be found . In this way , “ high blood pressure ” is mapped to hypertension . The brand drug name Aleve® is automatically mapped to naproxen . “ Non - insulin - dependent diabetes ” is mapped to ...
... mapped to HYPERCHOLESTEROL- EMIA ( abnormally high levels of cholesterol in the blood ) , but the “ cholesterol ” part of the entry phrase is mapped to cholesterol ( the principal sterol of all higher animals ) . When you type “ blood ...
... database . Entry terms are mapped to likely MeSH terms , displayed in a summary format . Each of these MeSH terms can be viewed in a full display that shows its definition, allowable subheadings (as check boxes), entry terms 66 Chapter 3.