Luke McGuinness

# Background & motivation

[medRxiv](https://www.medrxiv.org/), as the preprint repository for
papers in the medical, clinical, and related health
sciences,^[1](#fn:1){.footnote-ref role="doc-noteref"}^ has become a
central source of new studies related to the COVID-19 pandemic. As a
result, more and more researchers have begun to include medRxiv in the
list of bibliographic databases they search as part of systematic
reviews, a type of study that aims to find and bring together all
available evidence on a topic in order to provide a comprehensive answer
to a research question.

However, the native search functionality available on the medRxiv
website is not suitable for use in systematic reviews due to a number of
limitations. This prompted the development of
[medrxivr](https://docs.ropensci.org/medrxivr), an R package which
provides access to, and tools for searching, medRxiv and bioRxiv
preprint metadata. This post will detail the limitations of the native
medRxiv search and summarize the key functionality of medrxivr via two
key use cases. Note that while medrxivr allows users to access and
search medrxiv and bioRxiv preprint metadata as both repositories are
run by the Cold Spring Harbor Laboratory and so have a similar API
endpoint/native website search functionality, all illustrating examples
in this post are taken from medRxiv.

## Limitations of medRxiv website search functionality

### Confusing information about Boolean operators

A key limitation of the native search on the medRxiv website comes from
the use of Boolean operators in search strings. Boolean operators
(*AND*, *OR*, *NOT*) are used by librarians to combine individual search
terms into specific complex search strings. For example, "(dementia OR
alzheimer)" would be expected to return any record containing either
"dementia" or "alzheimer". Combining this with a second topic via *AND*,
e.g. "(dementia OR alzheimer) AND (lipid)", should return any record
containing either "dementia" or "alzheimer" once it also contains the
term "lipid".

One of the key assumptions of Boolean operators is that the order of the
terms does not matter, i.e. that "(dementia OR alzheimer) AND lipids" is
equivalent to "(alzheimer OR dementia) AND lipids". However, running
these searches on the 13th Oct 2020, the first gives 133 results and the
second gives 126. It is not clear what is happening behind the scenes,
but there is potentially some weighting of terms based on proximity.
This means that key records may be missed based on the arbitrary
ordering of the search terms.

### Lack of reproducbility

One of the key tenets of systematic reviews is that literature searches
are transparent and reproducible - that is, that a certain search
strategy (e.g. "(dementia OR alzheimer) AND lipids") will provide the
same number of results each time it is performed. However, anecdotal
evidence from information specialists and medical librarians suggests
that medRxiv's web search returns inconsistent citations over time.

### No batch export

Any systematic reviewer will tell you that for a database to be useful
as an information source, the one thing it **needs** to have is an
option to export the results of a given search *en masse*. At present,
this is not something the medRxiv website supports.

This means that records returned by a search must be exported by hand in
small batches, an error-prone and time-consuming (not to mention
anger-inducing!) method of extracting citations from a bibliographic
database.

  

## Use cases

In light of these limitations, below are two key use cases for the
medrxivr package.

### Use case \#1 -- Get preprint metadata in R

The primary aim of medrxivr is to make it as easy as possible to get
cleaned preprint metadata into R. This enables a range of exploratory
analysis to be readily performed. For example, to explore the
distribution of topic categories for those preprints posted to medRxiv
in the first week of 2020, the following code can be used:

::: {.highlight}
``` {.chroma tabindex="0"}
# Load the packageslibrary(medrxivr)library(dplyr)library(ggplot2)# Import the data from medRxiv API endpointmx_data <- mx_api_content(from_date = "2020-01-01", to_date = "2020-01-07")# Create the graph mx_data %>%  group_by(category) %>%  summarise(N = n(), .groups = "keep") %>%  arrange(desc(N)) %>%  ggplot(aes(y = reorder(category,N), x = N)) +  geom_col(colour = "black") +   ylab("Category") +  xlab("Number of preprints, 1st week of January 2020") +  scale_x_continuous(expand = c(0, 0), limits = c(0,10)) +  theme_classic() 
```
:::

<figure>
<img src="categories-1.png" width="600"
alt="Distribution of preprints posted to medRxiv in the first week of 2020 across topic categories." />
</figure>

### Use case \#2 -- Reproducible searching and exporting

While being able to easily import medRxiv data is useful, as a
systematic reviewer, my main interest is in what medrxivr allows you to
do once you have imported a local copy of the database. As mentioned in
the section above, the native medRxiv website search interface is
neither reproducible nor transparent, and while the API (accessed via
`mx_api_content()`) is great for accessing medRxiv metadata, it does not
offer any search functionality. The second core aspect of medrxivr's
functionality is designed to address these limitations, allowing
information specialists to build complex searches and apply them to a
local copy of the medRxiv database, all while documenting their search
strategy in a transparent manner. An example is given below, where a
researcher is looking for records that contain both dementia-related and
lipid-related terms:

::: {.highlight}
``` {.chroma tabindex="0"}
# Use the maintained snapshot to quickly load today's copy of the medRxiv database# Note - this `preprint_data` object could be saved as a CSV in your project #        repository to aid reproducibilitypreprint_data <- mx_snapshot()
```
:::

``` {tabindex="0"}
## Using medRxiv snapshot - 2020-10-19 00:32
```

::: {.highlight}
``` {.chroma tabindex="0"}
# Build your searchtopic1  <- c("dementia","vascular","alzheimer's")  # Combined with Boolean ORtopic2  <- c("lipids","statins","cholesterol")     # Combined with Boolean ORmyquery <- list(topic1, topic2)                    # Combined with Boolean AND# Run your searchresults <- mx_search(data = preprint_data,                     query = myquery)
```
:::

``` {tabindex="0"}
## Found 48 record(s) matching your search.
```

::: {.highlight}
``` {.chroma tabindex="0"}
# Let's have a look at the returned recordsresults
```
:::

``` {tabindex="0"}
## # A tibble: 48 x 14##       ID title abstract authors date       category doi   version author_correspo~ author_correspo~##    <dbl> <chr> <chr>    <chr>   <date>     <chr>    <chr>   <dbl> <chr>            <chr>           ##  1   184 Exce~ "Object~ De Lim~ 2019-07-25 Cardiov~ 10.1~       1 Andrei C Sposito "Laboratory of ~##  2   281 Peri~ "Introd~ Newton~ 2019-08-14 Addicti~ 10.1~       1 Dwight F Newton  "University of ~##  3   284 A co~ "Object~ Moriar~ 2019-08-15 Cardiov~ 10.1~       1 Mark H Ebell     "University of ~##  4   341 Apol~ "Backgr~ Richar~ 2019-08-29 Cardiov~ 10.1~       1 Tom G Richardson "University of ~##  5   307 Comp~ "Owing ~ Batty,~ 2019-08-17 Epidemi~ 10.1~       1 George David Ba~ "University Col~##  6   473 Self~ "Backgr~ Grover~ 2019-09-23 Cardiov~ 10.1~       1 Abhinav Grover   "University of ~##  7   643 Tren~ "Object~ Curtis~ 2019-10-18 Cardiov~ 10.1~       1 Ben Goldacre     "University of ~##  8   978 Does~ "OBJECT~ Harber~ 2019-11-29 Endocri~ 10.1~       1 Lisa Harber-Asc~ "King\\'s Colle~##  9  1085 Asse~ "Backgr~ Finner~ 2019-12-14 Cardiov~ 10.1~       1 Pradeep Nataraj~ "Massachusetts ~## 10  1333 Idea~ "BACKGR~ Mckenz~ 2020-01-16 Epidemi~ 10.1~       1 Trevor S Fergus~ "Caribbean Inst~## # ... with 38 more rows, and 4 more variables: link_page <chr>, link_pdf <chr>, license <chr>,## #   published <chr>
```

Once you have run your search, exporting the results to a .BIB file for
import into a reference manager, such as Zotero or Mendeley, is as
simple as passing the results object to the `mx_export()` function.

Similarly, as screening the full text PDFs of records returned by a
search against eligibility criteria forms a key part of a systematic
review, a second helper function downloads the PDF for each record
returned by `mx_search()`. Again, it is simply a case of passing the
`results` object to the `mx_download()` function.

## Conclusion

medRxiv is a fantastic resource and has been a key source of information
related to the COVID-19 pandemic. However, some key issues with the
website mean that its native search functionality precludes its use in
systematic reviews. The medrxivr R package seeks to address these
limitations by providing a user-friendly way to import and
systematically search medRxiv and bioRxiv records in R. Full
documentation of the package functionality, particularly around the
implementation of complex search strategies using syntax such as
wildcards and the NEAR operator, which is used to find co-located terms
(e.g. "cholesterol NEAR2 test" find records where "cholesterol" and
"test" are separated by two or less words), is available from the
[medrxivr website](https://docs.ropensci.org/medrxivr/). Finally, I
wanted to note that during development, medrxivr benefited immensely
from an [rOpenSci peer
review](https://github.com/ropensci/software-review/issues/380) by
[Tuija Sonkkila](https://github.com/tts) and [Najko
Jahn](https://github.com/njahn82).

::: {.footnotes role="doc-endnotes"}

------------------------------------------------------------------------

1.  ::: {#fn:1}
    Rawlinson, C., & Bloom, T. (2019). New preprint server for medical
    research. BMJ, 365.
    <https://doi.org/doi:10.1136/bmj.l2301> [↩︎](#fnref:1){.footnote-backref
    role="doc-backlink"}
    :::
:::


Background &amp; motivation  medRxiv, as the preprint repository for papers in the medical, clinical, and related health sciences,
<sup>
 1
</sup>
has become a central source of new studies related to the COVID-19 pandemic.

Postagens de Rogue Scholar

Searching medRxiv and bioRxiv Preprint Data