How many records are in Medline?

This question was asked recently in the expert searching e-list. This is a frequent question and one that remains of interest. I first wrote this blog post in 2006 so it is time to update.

The new PubMed is currently running alongside legacy PubMed so I tried these PubMed searches in both. Theoretically the results should be the same. To find out how many systematic reviews are in PubMed’s Systematic Reviews database subset, type: systematic[sb]. New PubMed: 145802 Legacy PubMed: 145870. I then tried the search filter that systematic[sb] is short for. Results: New PubMed: 145766 Legacy PubMed: 145870 Why is this happening? I’ll post this to expert searching e-list to see if I can get any answers.

I did the same experiment for the Clinical Queries RCT therapy/narrow focus filter by copying the search strategy into the search box: New PubMed: 545503 Legacy PubMed: 545543. Different again!

What about finding full text? In the PubMed search box, type: full text [sb] New PubMed: 21453189 Legacy PubMed: 21455734. Something strange is going on, right? Can someone else do these experiments and report in the comments?

To find out how many records there are in OVID Medline, type: docz.dz The current number is 30551310. The PubMed homepage says the database comprises over 30 million records. Last October, it was over 29 million!!

 

Database filters for Human studies

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.
AN: 1986023234

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).

ISSG Filters

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:
https://sites.google.com/a/york.ac.uk/issg-search-filters-resource/home