The new iteration of PubMed makes it inadvisable for building searches to inform systematic reviews. Why is this? The new version uses machine learning algorithms working behind the scenes which are invisible to the searcher. That means that transparency and reproducibility is no longer possible. Transparency and reproducibility are of key importance in scientific reporting and experiments. Without these present in the search strategy, a systematic review falls at the first hurdle when being critically appraised.
PRISMA-S was launched recently, outlining all the reporting requirements for literature searching in systematic reviews. Item 8 is: Include the search strategies for each database and information source, copied and pasted exactly as run. Note ‘exactly as run’. This is not possible in PubMed. Medline on the OVID platform (or via EBSCO or other aggregator) is preferred.
Searching Medline via a database aggregator platform has been the preferred practice for building and running search strategies for systematic reviews for over two decades now, mostly because of the ability to use proximity operators. Proximity operators are not available in PubMed and there are no plans to introduce them.
So, can you use PubMed at all? You can use it to search PMC articles, which can be useful for surgical (and other) questions. Care still needs to be taken though, and make sure you capture your search before logging off – PubMed no longer stores search histories.
It is common practice now to use the NOT animals not humans/ search string to exclude animal studies in Medline. But in Embase, such neat filter doesn’t work. Humans used not to be included under the Animal heading in Embase (why? Humans are animals – this is one of my bugbears). Now it is, which makes more sense. However, limiting to human (or human+animal) studies isn’t straightforward.
1 your subject search bottom line
2 exp animal/ or exp invertebrate/ or animal experiment/ or animal model/ or animal tissue/ or animal cell/ or nonhuman/
3 exp human/ or human cell/
4 2 and 3
5 2 not 4
6 1 not 5
exp human has normal human/ as a narrower term and the synonyms suggest that this means a healthy person without disease. nonhuman/ is a curious term – the scope is: “Used for all items on non-human organisms (animals, bacteria, viruses, plants etc.) or on tissue, cells or cell components from such organisms”. Why there is a need to group all these together under nonhuman when you could search for virus or bacteria or frog* (example that comes to mind for no reason I can think of) beats me. Also curious is exp animal/ doesn’t include invertebrates.
Jacqueline Limpens provided another alternative to searching for human studies in Embase, which she posted on the expertsearching e-list:
1 your subject search bottom line
2 (exp animal/ or animal.hw. or nonhuman/) not (exp human/ or human cell/ or (human or humans).ti.)
3 1 not 2
She wrote that for a particular search she was doing, animal.hw. (heading word) also found animal embryo (which gave noise especially in this IVF & embryo transfer-media topic). human or humans in the title field had to be added to avoid losing this relevant paper:
Improved pregnancy rate in human in vitro fertilization with the use of a medium based on the composition of human tubal fluid.
Quinn P., Kerin J.F., Warnes G.M.
Fertility and Sterility. 44 (4) (pp 493-498), 1985. Date of Publication: 1985.
It isn’t indexed with human, but it is with pregnancy and looking at the scope note for pregnancy, it implies human pregnancy BUT it could include any animal pregnancy – confusing! She also mentioned that you should consider other headings that could be indicative of non-human disease, like exp experimental neoplasm/ and xenografts/.
With Embase, it seems a wise idea to scan/search the results before you add any human studies filter to see if there are any relevant titles that could be excluded with it applied (as in the above pregnancy article).
The InterTASC Information Specialists’ Sub-Group (ISSG) Search Filter Resource is a collection of tested and untested filters (inclusion is not endorsement). As well as filters for a variety of databases and database interfaces for all sorts of studies and topics, it includes information about how to critically appraise filters and how filters are developed. There are in-built filters in some databases and this resource has a nice round up of them.
Filters are used at the end of complex searches to restrict retrieval and are either sensitive (maximise recall – for a particular study type for example), or precision (aimed to reduce noise). The choice of which to use is always governed by the research question. If you work in search strategy design for systematic reviews or health technology assessments, knowledge about filters is essential. And if you are new to this area, it is worth playing with different filters against a single strategy to see how they work (this is also good practice if you are weighing up different filters before making a final choice).
ISSG Search Filter Resource [Internet]. Glanville J, Lefebvre C, Wright K, editors. York (UK): The InterTASC Information Specialists’ Sub-Group; 2008 [updated 2019 August 12; cited 31 October 2019]. Available from: