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Last updated: 2020-11-13

Checks: 2 0

Knit directory: website/

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R Markdown file: up-to-date

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Repository version: 8e98482

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    Ignored:    .Rhistory
    Ignored:    .Rproj.user/

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    Modified:   .gitignore

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/index.Rmd) and HTML (docs/index.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
html	ed31ccb	L-ENA	2020-11-06	Build site.
html	a34fbc8	L-ENA	2020-10-30	Build site.
html	b5505f2	L-ENA	2020-10-30	Build site.
Rmd	e826d62	L-ENA	2020-10-30	Updated index
html	e826d62	L-ENA	2020-10-30	Updated index
html	93bdfca	L-ENA	2020-10-30	Build site.
html	7bafffe	L-ENA	2020-10-30	Build site.
html	26e1d9b	L-ENA	2020-10-30	Build site.
html	c16ec66	L-ENA	2020-10-30	Build site.
html	c86cb34	L-ENA	2020-10-30	Build site.
html	e727571	L-ENA	2020-10-30	Build site.
html	798d57b	L-ENA	2020-10-30	Build site.
html	7074ef2	L-ENA	2020-10-30	Build site.
Rmd	aa66c47	L-ENA	2020-10-30	Customised about/publications etc
html	02b0296	L-ENA	2020-10-30	Build site.
html	5061dd3	L-ENA	2020-10-30	Build site.
html	581260b	L-ENA	2020-10-30	Build site.
Rmd	7fbed26	L-ENA	2020-10-30	Start workflowr project.

Abstract of the protocol

Background:

Researchers in evidence-based medicine cannot keep up with the amounts of both old and newly published primary research articles. Support for the early stages of the systematic review process – searching and screening studies for eligibility – is necessary because it is currently impossible to search for relevant research with precision. Better automated data extraction may not only facilitate the stage of review traditionally labelled ‘data extraction’, but also change earlier phases of the review process by making it possible to identify relevant research. Exponential improvements in computational processing speed and data storage are fostering the development of data mining models and algorithms. This, in combination with quicker pathways to publication, led to a large landscape of tools and methods for data mining and extraction.

Objective:

To review published methods and tools for data extraction to (semi)automate the systematic reviewing process.

Methods:

We propose to conduct a living review. With this methodology we aim to do constant evidence surveillance, bi-monthly search updates, as well as review updates every 6 months if new evidence permits it. In a cross-sectional analysis we will extract methodological characteristics and assess the quality of reporting in our included papers.

Conclusions: