Abstract
ObjectiveThe PubMed Clinical Study Category filters are subdivided into “Broad” and “Narrow” versions that are designed to maximize either sensitivity or specificity by using two different sets of keywords and Medical Subject Headings (MeSH). A searcher might assume that all items retrieved by Narrow would also be found by Broad, but there are occasions when some [Filter name]/Narrow citations are missed when using [Filter name]/Broad alone. This study quantifies the size of this effect.MethodsFor each of the five Clinical Study Categories, PubMed was searched for citations matching the query Filter/Narrow NOT Filter/Broad. This number was compared with that for Filter/Broad to compute the number of Narrow citations missed per 1,000 Broad. This process was repeated for the MeSH terms for “Medicine” and “Diseases,” as well as for a set of individual test searches.ResultsThe Clinical Study Category filters for Etiology, Clinical Prediction Guides, Diagnosis, and Prognosis all showed notable numbers of Filter/Narrow citations that were missed when searching Filter/Broad alone. This was particularly true for Prognosis, where a searcher could easily miss one Prognosis/Narrow citation for every ten Prognosis/Broad citations retrieved.ConclusionsUsers of the Clinical Study Category filters (except for Therapy) should consider combining Filter/Narrow together with Filter/Broad in their search strategy. This is particularly true when using Prognosis/Broad, as otherwise there is a substantial risk of missing potentially relevant citations.
Highlights
It is widely appreciated that the rapid proliferation of the biomedical literature has made it increasingly difficult for health care practitioners to efficiently find the information that they need to do their jobs
The significance of missing a given number of potentially relevant citations for a search depends in part on the size of that search’s total result set
While any database filter is approximate in its effect, both the Broad and Narrow iterations of the Clinical Study Categories are designed to maximize the amount of clinically relevant material for a given search
Summary
It is widely appreciated that the rapid proliferation of the biomedical literature has made it increasingly difficult for health care practitioners to efficiently find the information that they need to do their jobs. In large bibliographic databases such as MEDLINE, the sorts of rigorous clinical studies that might inform clinical decision-making are mixed together with a host of letters, case reports, and notes from the frontiers of bench science. This poses a challenge to full-time clinicians, who typically have minimal training in bibliographic searching and very little time to devote to locating resources. Hedges are sometimes used by librarians when performing complex searches, but it is unlikely that non-information professionals would use these tools unless they were made explicitly aware of them. It is to this end that the National Library of Medicine has linked a tool called the “PubMed Clinical Queries” from the front page of PubMed [1]
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