Efficient bibliographic searches on allergology using PubMed.
BACKGROUND
PubMed is the most important of the non-specialized databases on biomedical literature. International and quickly updated is elaborated by the American Government and contains only information about papers published in scientific journal/s. Although it can be used as an unique Data Base, as a matter of fact is the addition of several subgroups (among them MEDLINE) that can be searched simultaneously.


OBJECTIVE
To present the main characteristics of PubMed, as well as the most important procedures of search, for obtaining efficient results in searches on allergology.
PubMed is elaborated by the American Administration, that condition the character of the registered papers, 90 % of them are written in English in American (50 %) or British (20 %) Journals. Because of this, the information for certain specialties or countries must be obtained from other sources. This paper shows how PubMed allows to search in natural language due to the Automatic Term Mapping that links terms from the natural language with the descriptors producing searches with a higher sensitivity although with a low specificity. Nevertheless the MeSH (Medical Subject Headings) thesaurus allows to translate those terms from the natural language to the equivalent descriptor, as well as to make queries in the PubMed's documental language with a high specificity but with lower sensitivity than the natural language. The use of union (OR), intersection (AND) and exclusion (NOT) operators, as well as tags, such as delimiters of the search fields, allows to increase the specificity of the results. Similar results may be obtained with the use of Limits. Searches done using Clinical Queries are very interesting due to their direct clinical application and because allow to find systematic reviews, metaanalysis or clinically oriented papers (treatment, diagnostic, etiology, prognosis or clinical prediction guides) on the area of interest. Other procedures such as the Index, History of searches, and the widening of the selection using Related Articles and the storing of separate results in the Clipboard to be kept by the user, are presented in this paper.
Department of Social and Health Sciences. University of Murcia Medical School. Spain. jmsaez@um.es
J M
JM
Sáez Gómez
E
E
Aguinaga Ontoso
J M
JM
Negro Alvarez
F
F
Guillén-Grima
J C
JC
Ivancevich
C M
CM
Bozzola
D000043:Abstracting and Indexing; D000486:Allergy and Immunology / Q000193:education; D001634:Bibliographies as Topic; D016247:Information Storage and Retrieval; D007256:Information Systems / Q000706:statistics & numerical data; D016242:Library Automation; D016239:MEDLINE / Q000706:statistics & numerical data*; D046650:Medical Subject Headings / Q000706:statistics & numerical data*; D009323:Natural Language Processing; D039781:PubMed / Q000706:statistics & numerical data*; D013358:Subject Headings; D017432:Unified Medical Language System