PubMed · 11984108
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Ann B Hamric. 2002. Dealing with the knowledge explosion.. https://doi.org/10.1097/00002800-200203000-00006
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BACKGROUND: Published systematic reviews on prediction of preterm birth have tended to focus on a limited number of tests and their search strategies have often been relatively simple. Evaluation of all available tests in a systemic review will require a broad search strategy. AIM: To describe a case study of electronic searching for a systematic review of accuracy studies evaluating all tests for predicting preterm birth. METHODS: The search strategy, developed to capture literatures concerning all the tests en-masse consisted of formulation of an appropriate combination of search terms, pilot searches to refine the search term combination, selection of relevant databases, and citation retrieval from the refined searches for selection of potentially relevant papers. Electronic searches were carried out on general bibliographic databases (Biosis, Embase, Medline, Pascal and Scisearch), specialised databases (Database of Abstracts of Reviews of Effectiveness, Medion, National Research Register, Cochrane Controlled Trial Register and Cochrane Database of Systematic Reviews). RESULTS: A total of 30076 citations were identified. Of these 8855 (29%) citations were duplications either within a database or across databases. Of the remaining 21221 citations, 3333 were considered potentially relevant to the review after assessment by two reviewers. These citations covered 19 different tests for predicting preterm birth. CONCLUSIONS: This case study suggests that with use of a concerted effort to organise and manage the electronic searching it is feasible to undertake broad searches for systematic reviews with multiple questions.
The journal Impact Factor and citation counts are misconstrued and misused as measures of scientific quality. Articles must be read in order to judge their quality. We have introduced a system, which may be easily replicated, to identify the best articles published in a journal.
The availability of scientific bibliographies through online databases provides a rich source of information for scientists to support their research. However, the risk of this pervasive availability is that an individual researcher may fail to find relevant information that is outside the direct scope of interest. Following Swanson's ABC model of disjoint but complementary structures in the biomedical literature, we have developed a discovery support tool to systematically analyze the scientific literature in order to generate novel and plausible hypotheses. In this case report, we employ the system to find potentially new target diseases for the drug thalidomide. We find solid bibliographic evidence suggesting that thalidomide might be useful for treating acute pancreatitis, chronic hepatitis C, Helicobacter pylori-induced gastritis, and myasthenia gravis. However, experimental and clinical evaluation is needed to validate these hypotheses and to assess the trade-off between therapeutic benefits and toxicities.