Academic journal article Journal of the Medical Library Association

Search Strategies to Identify Information on Adverse Effects: A Systematic Review *

Academic journal article Journal of the Medical Library Association

Search Strategies to Identify Information on Adverse Effects: A Systematic Review *

Article excerpt

Objectives: The review evaluated studies of electronic database search strategies designed to retrieve adverse effects data for systematic reviews.

Methods: Studies of adverse effects were located in ten databases as well as by checking references, hand-searching, searching citations, and contacting experts. Two reviewers screened the retrieved records for potentially relevant papers.

Results: Five thousand three hundred thirteen citations were retrieved, yielding 19 studies designed to develop or evaluate adverse effect filters, of which 3 met the inclusion criteria. All 3 studies identified highly sensitive search strategies capable of retrieving over 95% of relevant records. However, 1 study did not evaluate precision, while the level of precision in the other 2 studies ranged from 0.8% to 2.8%. Methodological issues in these papers included the relatively small number of records, absence of a validation set of records for testing, and limited evaluation of precision.

Conclusions: The results indicate the difficulty of achieving highly sensitive searches for information on adverse effects with a reasonable level of precision. Researchers who intend to locate studies on adverse effects should allow for the amount of resources and time required to conduct a highly sensitive search.


For patients, clinicians, and other decision makers to make informed, balanced decisions, they need appropriate information on both the intended benefits and undesirable consequences of an intervention. However, currently there is an absence of sufficient evidence-based information on the frequency or magnitude of adverse effects. Long lists of potential adverse effects may be all that can be found, with little or no information available as to the magnitude of these effects or of the probability of their occurrence [1-3]. One potential solution to this problem would be to incorporate data on adverse effects into systematic reviews. Systematic reviews are one of the most powerful and reliable tools to estimate the magnitude of effects and the probability of their occurrence [4-10].

Searching databases as part of a systematic review can be a difficult and time-consuming process and usually requires the skills of an information specialist or experienced searcher. Search strategies need to be devised that balance sensitivity (the ability to identify as many relevant articles as possible) with precision (the ability to exclude as many irrelevant articles as possible). In recent years, research has been undertaken to improve this process by developing search filters or search hedges [11-14]. A search filter is a predefined combination of search terms designed to retrieve information on a particular topic. The filter may be created and evaluated in various ways. For example, search terms in a filter may be subjectively derived by contacting experts in literature searching or the topic area. Terms may be objectively derived using word frequency analysis or statistical analysis of a set of relevant records, and then the best combination of terms can be identified by measuring how many relevant and irrelevant records are retrieved using various combinations. Alternatively, statistical techniques such as logistic regression can be used to suggest the best combination of search terms. Once a search filter has been developed, it can then be tested against a validation set of records (a different set of relevant records).

Methodological search filters have been developed for various study designs and have proved to be particularly useful for effectiveness studies [12, 1418]. For example, the Cochrane Collaboration uses a highly sensitive search strategy that has recently been updated for identifying reports of randomized trials [14, 16]. In PubMed, the Clinical Queries feature allows searchers to filter articles according to etiology, diagnosis, prognosis, therapy, or clinical prediction guides [19]. …

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