Boolean Operators (AND, OR, NOT) are tools for combining search terms and are inherent part of online database searching. While experienced searchers will use Boolean Operators directly in their search strategies, even novice searchers that just enter a string of terms into a database’s search box will end up indirectly using the Boolean operator AND, as each space between words will be treated by the database as AND, thus combining each term together into a search strategy that would retrieve results that have all terms present.
Most search strategies will either use just AND or a combination of both AND and OR. The third Boolean operator, NOT, is much more complicated and requires some understanding to use properly in a search.
Using the Boolean Operator NOT
The Boolean operator NOT can be used when a term or terms needs to be excluded from your search strategy.
For example, if you were interested in articles that looked at children with cancer, but you did not want articles that looked specifically at infants, you could create a search strategy like this:
cancer AND child* NOT infant* — or — (cancer AND child*) NOT infant*
The Problem with NOT
When using the Boolean operator NOT to exclude terms, it can become problematic when the database excludes records that contain both the term(s) you want to exclude and the term(s) you want in your search.
In the above example, not only articles about cancer in infants will be excluded from the results but it will also exclude any articles about cancer in both children and infants.
Information professionals (librarians and informationists) advise using the Boolean operator NOT with extreme caution when conducting searches. It’s better to reach out to an information professional for assistance with complex search techniques and how to best proceed with a search when there is a term you want to avoid.
Variations Across Databases
Not all databases function the same way, and using the Boolean operator NOT is no different. While most databases allow for using simply NOT to exclude terms, depending on the database or platform, you might need to use the operator AND NOT instead (Scopus), or once the search is performed use the Exclude button found within the Refine Search panel (also in Scopus).
Takeaway
The Boolean operator NOT should be used with extreme caution. It is best to consult a Librarian on its use in your search.
The amount of published biomedical literature has been growing exponentially for decades, and that trend is not slowing down anytime soon. With this explosion of published content, it can be overwhelming to find exactly what you are looking for.
The 21st Century Digital Age
The start of the 21st century was heralded as the “digital age”, and the growth of content shifted from a linear to an exponential growth model. There were approximately 13 million citations in PubMed at the start of the 21st century. Within the first decade that number rose to 20 million. Today there are over 36 million citations in PubMed.
Growth of PubMed citations from 1986 to 2010 Source: Lu Z. PubMed and beyond: a survey of web tools for searching biomedical literature. Database (Oxford). 2011;2011:baq036. Published 2011 Jan 18.
Zhiyong Lu, from the National Center for Biotechnology Information (NCBI), wrote about going beyond PubMed back in 2011, and shared an initial overview of web-based tools available that work alongside or on top of PubMed to provide more search functionality to users.
From the Digital Age to the Age of Artificial Intelligence
Today, as we now inch closer to the quarter-century point, digital technology has literally begun taking on a life of its own. With the advent of machine-learning and generative artificial intelligence, suddenly technology itself can create its own content! And while there are plenty of ethical issues surrounding the use and abuse of AI that cover nearly all aspects of life, this technology allows for considerable benefits as well.
New tools have emerged to help us better navigate, digest, and synthesize the overwhelming amount of digital information available, including biomedical literature. Many of these tools are web-based resources that either overlay or work in conjunction with PubMed to provide functionality that goes beyond basic search and retrieval.
Overview of five specialised search scenarios in biomedicine Source: Jin Q, Leaman R, Lu Z. PubMed and beyond: biomedical literature search in the age of artificial intelligence. EBioMedicine. 2024;100:104988.
They looked at five specific types of specialized search needs, and addressed the various tools and resources that can provide necessary functionality to support those search needs: evidence-based medicine, precision medicine, semantic searching, recommendations, and text mining.
Harness Technology with these Search Tools
Using these five identified search needs categories, below are selected resources to assist users in navigating and digesting the ever-expanding field of biomedical research.
The JBI EBP Database provides the latest research and evidence-based guidelines regarding patient care, treatment options, and interventions to empower clinicians and healthcare administrators to make informed, confident decisions.
TRIP is a clinical search engine designed to allow users to quickly and easily find and use high-quality research evidence to support their practice and/or care.
LitVar normalizes different forms of the same variant into a unique and standardized name so that all matching articles can be returned regardless of the use of a specific name in the query.
DigSee is a text mining search engine to provide evidence sentences describing that “genes” are involved in the development of “disease” through “biological events”. With a query of (disease, genes, events), Medline abstracts with highlighted evidence sentences will be retrieved.
LitSense is a unique search system for making sense of the biomedical literature at the sentence level. Given a query, LitSense finds the best-matching sentences based on overlapping terms as well as semantic similarity via a cutting-edge neural embedding approach.
PubTator Central (PTC) is a Web-based system providing automatic annotations of biomedical concepts such as genes and mutations in PubMed abstracts and PMC full-text articles.
PubMedKB combines a multitude of state-of-the-art text-mining tools optimized to automatically identify the complex relationships between biomedical entities in the PubMed abstracts.
As technology evolves, so will the research environment, and it’s imperative that we are able to leverage technology to keep up. It’s also important to understand these new technologies, how they work, and how they can be used to make work more efficient. But it’s also important to understand their limitations and the ethical issues that could arise when using these technologies without further human insight.
While not necessarily “secret”, field codes are an underutilized feature found in nearly all scholarly literature databases.
A field is a specific part of a record found within a database. A field code (also sometimes referred to as a field tag) is a word, abbreviation, or letters that are tied to a specific field within a record. Some common examples of fields are: title, author, and publication year. Every database has their own set of field codes for the various fields found in their records. Here we discuss how you can leverage these field codes to focus your literature searches.
Adding Field Codes to your Search
The default, or basic search, in most databases, is a general all field or keyword search. While this may be fine for simple searches, as searches become more complex, sometimes there needs to be more options available. That is where field codes come in.
The Advanced Search features found in most databases is where you can manipulate your search strategies to include field codes. In some cases available or common field codes are listed on this page. Depending on the database, you may or may not be able to add multiple field codes to a single search. If you are limited to a single field code, use the Boolean Operator OR to combine multiple search strings.
