Last updated: 2021-08-04
Checks: 2 0
Knit directory: website/
This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 1399ad6 | Lena Schmidt | 2021-08-03 | Build site. |
| html | 2569cc1 | Lena Schmidt | 2021-08-03 | Build site. |
| html | e022fc2 | Lena Schmidt | 2021-08-03 | Build site. |
| html | 3674a0f | Lena Schmidt | 2021-07-07 | Build site. |
| html | 5bad23b | Lena Schmidt | 2021-07-07 | Build site. |
| html | 6b05077 | Lena Schmidt | 2021-05-20 | Build site. |
| html | 821a005 | Lena Schmidt | 2021-05-20 | Build site. |
| html | 1766206 | Lena Schmidt | 2021-05-20 | Build site. |
| html | c31a64c | Lena Schmidt | 2021-05-19 | Build site. |
| html | db43913 | Lena Schmidt | 2021-05-19 | Build site. |
| Rmd | 0194d67 | Lena Schmidt | 2021-05-19 | Updated text after publication of base review, Updated at 2021-05-19 16:31:58 |
| html | 0194d67 | Lena Schmidt | 2021-05-19 | Updated text after publication of base review, Updated at 2021-05-19 16:31:58 |
| html | 21b1c0f | L-ENA | 2020-11-13 | Build site. |
| html | 45aa0d5 | L-ENA | 2020-11-13 | Build site. |
| 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. |
This living review looks at data extraction methods for systematic review (semi)automation. On this website you will find the latest updates to the review, as well as additional information about the team and related publication of this review and its software.
The reliable and usable (semi)automation of data extraction can support the field of systematic review by reducing the workload required to gather information about the conduct and results of the included studies. This living systematic review examines published approaches for data extraction from reports of clinical studies
We systematically and continually search MEDLINE, Institute of Electrical and Electronics Engineers (IEEE), arXiv, and the dblp computer science bibliography databases. Full text screening and data extraction are conducted within an open-source living systematic review application created for the purpose of this review. This iteration of the living review includes publications up to a cut-off date of 22 April 2020.
In total, 53 publications are included in this version of our review. Of these, 41 (77%) of the publications addressed extraction of data from abstracts, while 14 (26%) used full texts. A total of 48 (90%) publications developed and evaluated classifiers that used randomised controlled trials as the main target texts. Over 30 entities were extracted, with PICOs (population, intervention, comparator, outcome) being the most frequently extracted. A description of their datasets was provided by 49 publications (94%), but only seven (13%) made the data publicly available. Code was made available by 10 (19%) publications, and five (9%) implemented publicly available tools.
This living systematic review presents an overview of (semi)automated data-extraction literature of interest to different types of systematic review. We identified a broad evidence base of publications describing data extraction for interventional reviews and a small number of publications extracting epidemiological or diagnostic accuracy data. The lack of publicly available gold-standard data for evaluation, and lack of application thereof, makes it difficult to draw conclusions on which is the best-performing system for each data extraction target. With this living review we aim to review the literature continually.