Identifying search terms

Standardised search terms are thesaurus and indexing terms that are used by electronic databases as a convenient way to categorise articles, allowing for efficient searching. Individual database records may be assigned several different standardised search terms that describe the same or similar concepts (e.g. bipolar disorder, bipolar depression, manic–depressive psychosis, mania). This has the advantage that even if the original article did not use the standardised term, when the article is catalogued in a database it is allocated that term (Guaiana Reference Guaiana, Barbui and Cipriani2010). For example, an older paper might refer to ‘manic depression’, but would be categorised under the term ‘bipolar disorder’ when catalogued in MEDLINE. These standardised search terms are called MeSH (medical subject headings) in MEDLINE and PubMed, and Emtree in Embase, and are organised in a hierarchal structure (Fig. 1). In both MEDLINE and Embase an ‘explode’ command enables the database to search for a requested term, as well as specific related terms. Both narrow and broader search terms can be viewed and selected to be included in the search if appropriate to a topic. The Yale MeSH Analyzer tool (mesh.med.yale.edu) can be used to help identify potential terms and phrases to include in a search. It is also useful to understand why relevant articles may be missing from an initial search, as it produces a comparison grid of MeSH terms used to index each article (see Box 1 for a tutorial video link).

FIG 1 Search terms and hierarchical structure of MeSH (medical subject heading) in MEDLINE and PubMed.

In addition, MEDLINE also distinguishes between MeSH headings (MH) and publication type (PT) terms. Publication terms are less about the content of an article than about its type, specifying for example a review article, meta-analysis or RCT.

Both MeSH and Emtree have their own peculiarities, with variations in thesaurus and indexing terms. In addition, not all concepts are assigned standardised search terms, and not all databases use this method of indexing the literature. It is advisable to check the guidelines of selected databases before undertaking a search. In the absence of a MeSH heading for a particular term, free-text terms could be used.

Free-text terms are used in natural language and are not part of a database’s controlled vocabulary. Free-text terms can be used in addition to standardised search terms in order to identify as many relevant records as possible (Higgins Reference Higgins and Green2011). Using free-text terms allows the reviewer to search using variations in language or spelling (e.g. hypomani* or mania* or manic* – see truncation and wildcard functions below and Fig. 2). A disadvantage of free-text terms is that they are only searched for in the title and abstracts of database records, and not in the full texts, meaning that when a free-text word is used only in the body of an article, it will not be retrieved in the search. Additionally, a number of specific considerations should be taken into account when selecting and using free-text terms:

• • synonyms, related terms and alternative phrases (e.g. mood instability, affective instability, mood lability or emotion dysregulation)

• • abbreviations or acronyms in medical and scientific research (e.g. magnetic resonance imaging or MRI)

• • lay and medical terminology (e.g. high blood pressure or hypertension)

• • brand and generic drug names (e.g. Prozac or fluoxetine)

• • variants in spelling (e.g. UK English and American English: behaviour or behavior; paediatric or pediatric).

FIG 2 Example of a search strategy about bipolar disorder using MEDLINE (Cipriani Reference Cipriani, Saunders and Attenburrow2016a). The strategy follows the PICO framework and includes MeSH terms, free-text keywords and a number of other techniques, such as truncation, that have been outlined in this article. Numbers in bold give the number of citations retrieved by each search.

Truncation and wildcard functions can be used in most databases to capture variations in language:

• • truncation allows the stem of a word that may have variant endings to be searched: for example, a search for depress* uses truncation to retrieve articles that mention both depression and depressive; truncation symbols may vary by database, but common symbols include: *, ! and #

• • wild cards substitute one letter within a word to retrieve alternative spellings: for example, ‘wom?n’ would retrieve the terms ‘woman’ and ‘women’.

Combining search terms

Search terms should be combined in the search strategy using Boolean operators. Boolean operators allow standardised search terms and free-text terms to be combined. There are three main Boolean operators – AND, OR and NOT (Fig. 3).

• • OR – this operator is used to broaden a search, finding articles that contain at least one of the search terms within a concept. Sets of terms can be created for each concept, for example the population of interest: (bipolar disorder OR bipolar depression). Parentheses are used to build up search terms, with words within parentheses treated as a unit.

• • AND – this can be used to join sets of concepts together, narrowing the retrieved literature to articles that contain all concepts, for example the population or condition of interest and the intervention to be evaluated: (bipolar disorder OR bipolar depression) AND calcium channel blockers. However, if at least one term from each set of concepts is not identified from the title or abstract of an article, this article will not be identified by the search strategy. It is worth mentioning here that some databases can run the search also across the full texts. For example, ScienceDirect and most publishing houses allow this kind of search, which is much more comprehensive than abstract or title searches only.

• • NOT – this operator, used less often, can focus a search strategy so that it does not retrieve specific literature, for example human studies NOT animal studies. However, in certain cases the NOT operator can be too restrictive, for example if excluding male gender from a population, using ‘NOT male’ would also mean that any articles about both males and females are not obtained by the search.

FIG 3 Example of Boolean operator concepts (the resulting search is the light red shaded area).

The conventions of each database should be checked before undertaking a literature search, as functions and operators may differ slightly between them (Cipriani Reference Cipriani, Saunders and Attenburrow2016b). This is particularly relevant when using limits and filters. Figure 2 shows an example search strategy incorporating many of the concepts described above. The search strategy is taken from Cipriani et al (Reference Cipriani, Zhou and Del Giovane2016a), but simplified to include only one intervention.

Search filters

A number of filters exist to focus a search, including language, date and study design or study focus filters. Language filters can restrict retrieval of articles to the English language, although if language is not an inclusion criterion it should not be restricted, to avoid language bias. Date filters can be used to restrict the search to literature from a specified period, for example if an intervention was only made available after a certain date. In addition, if good systematic reviews exist that are likely to capture all relevant literature (as advised by an information specialist), date restrictions can be used to search additional literature published after the date of that included in the systematic review. In the same way, date filters can be used to update a literature search since the last time it was conducted. Reviewing the literature should be a timely process (new and potentially relevant evidence is produced constantly) and updating the search is an important step, especially if collecting evidence to inform clinical decision-making, as publications in the field of medicine are increasing at an impressive rate (Barber Reference Barber, Corsi and Furukawa2016). The filters chosen will depend on the research question and nature of evidence that is sought through the literature search and the guidelines of the individual database that is used.

Google Scholar

Google Scholar allows basic Boolean operators to be used in strings of search terms. However, the search engine does not use standardised search terms that have been tagged as in traditional databases and therefore variations of keywords should always be searched. There are advantages and disadvantages to using a web search engine such as Google Scholar. Google Scholar searches the full text of an article for keywords and also searches a wider range of sources, such as conference proceedings and books, that are not found in traditional databases, making it a good resource to search for grey literature (Haddaway Reference Haddaway, Collins and Coughlin2015). In addition, Google Scholar finds articles cited by other relevant articles produced in the search. However, variable retrieval of content (due to regular updating of Google algorithms and the individual's search history and location) means that search results are not necessarily reproducible and are therefore not in keeping with replicable search methods required by systematic reviews. Google Scholar alone has not been shown to retrieve more literature than other traditional databases discussed in this article and therefore should be used in addition to other sources (Bramer Reference Bramer, Giustini and Kramer2016).

Citation searching

Once the search strategy has identified relevant literature, the reference lists in these sources can be searched. This is called citation searching or backward searching, and it can be used to see where particular research topics led others. This method is particularly useful if the search identifies systematic reviews or meta-analyses of a similar topic.

Conference abstracts

Conference abstracts are considered ‘grey literature’, i.e. literature that is not formally published in journals or books (Alberani Reference Alberani, De Castro Pietrangeli and Mazza1990). Scherer and colleagues found that only 52.6% of all conference abstracts go on to full publication of results, and factors associated with publication were studies that had RCT designs and the reporting of positive or significant results (Scherer Reference Scherer, Langenberg and von Elm2007). Therefore, failure to search relevant grey literature might miss certain data and bias the results of a review. Although conference abstracts are not indexed in most major electronic databases, they are available in databases such as BIOSIS Previews (Box 1). However, as with many unpublished studies, these data did not undergo the peer review process that is often a tool for assessing and possibly improving the quality of the publication.

Searching trial registers and pharmaceutical websites

For reviews of trial interventions, a number of trial registers exist. ClinicalTrials.gov (clinicaltrials.gov) provides access to information on public and privately conducted clinical trials in humans. Results for both published and unpublished studies can be found for many trials on the register, in addition to information about studies that are ongoing. Searching each trial register requires a slightly different search strategy, but many of the basic principles described above still apply. Basic searches on ClinicialTrials.gov include searching by condition, specific drugs or interventions and these can be linked using Boolean operators: for example, (bipolar disorder OR manic depressive disorder) AND lithium. As mentioned above, parentheses can be used to build up search terms. More advanced searches allow one to specify further search fields such as the status of studies, study type and age of participants. The US Food and Drug Administration (FDA) hosts a database providing information about FDA-approved drugs, therapeutic products and devices (www.fda.gov). The database (with open access to anyone, not only in the USA) can be searched by the drug name, its active ingredient or its approval application number and, for most drugs approved in the past 20 years or so, a review of clinical trial results (some of which remain unpublished) used as evidence in the approval process is available. The European Medicines Agency (EMA) hosts a similar register for medicines developed for use in the European Union (www.ema.europa.eu). An internet search will show that many other national and international trial registers exist that, depending on the review question, may be relevant search sources. The World Health Organization International Clinical Trials Registry Platform (WHO ICTRP; www.who.int/ictrp) provides access to a central database bringing a number of these national and international trial registers together. It can be searched in much the same way as ClinicalTrials.gov.

A number of pharmaceutical companies now share data from company-sponsored clinical trials. GlaxoSmithKline (GSK) is transparent in the sharing of its data from clinical studies and hosts its own clinical study register (www.gsk-clinicalstudyregister.com). Eli-Lilly provides clinical trial results both on its website (www.lillytrialguide.com) and in external registries. However, other pharmaceutical companies, such as Wyeth and Roche, divert users to clinical trial results in external registries. These registries include both published and previously unpublished studies. Searching techniques differ for each company and hand-searching through documents is often required to identify studies.

Communication with authors

Direct communication with authors of published papers could produce both additional data omitted from published studies and other unpublished studies. Contact details are usually available for the corresponding author of each paper. Although high-quality reviews do make efforts to obtain and include unpublished data, this does have potential disadvantages: the data may be incomplete and are likely not to have been peer-reviewed. It is also important to note that, although reviewers should make every effort to find unpublished data in an effort to minimise publication bias, there is still likely to remain a degree of this bias in the studies selected for a systematic review.