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Systematic review automation technologies

Overview of attention for article published in Systematic Reviews, July 2014
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#34 of 985)
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

twitter
101 tweeters
peer_reviews
1 peer review site
googleplus
2 Google+ users
reddit
1 Redditor

Citations

dimensions_citation
78 Dimensions

Readers on

mendeley
236 Mendeley
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Title
Systematic review automation technologies
Published in
Systematic Reviews, July 2014
DOI 10.1186/2046-4053-3-74
Pubmed ID
Authors

Guy Tsafnat, Paul Glasziou, Miew Keen Choong, Adam Dunn, Filippo Galgani, Enrico Coiera, Tsafnat G, Glasziou P, Choong MK, Dunn A, Galgani F, Coiera E

Abstract

Systematic reviews, a cornerstone of evidence-based medicine, are not produced quickly enough to support clinical practice. The cost of production, availability of the requisite expertise and timeliness are often quoted as major contributors for the delay. This detailed survey of the state of the art of information systems designed to support or automate individual tasks in the systematic review, and in particular systematic reviews of randomized controlled clinical trials, reveals trends that see the convergence of several parallel research projects.We surveyed literature describing informatics systems that support or automate the processes of systematic review or each of the tasks of the systematic review. Several projects focus on automating, simplifying and/or streamlining specific tasks of the systematic review. Some tasks are already fully automated while others are still largely manual. In this review, we describe each task and the effect that its automation would have on the entire systematic review process, summarize the existing information system support for each task, and highlight where further research is needed for realizing automation for the task. Integration of the systems that automate systematic review tasks may lead to a revised systematic review workflow. We envisage the optimized workflow will lead to system in which each systematic review is described as a computer program that automatically retrieves relevant trials, appraises them, extracts and synthesizes data, evaluates the risk of bias, performs meta-analysis calculations, and produces a report in real time.

Twitter Demographics

The data shown below were collected from the profiles of 101 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 236 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 8 3%
United States 5 2%
Brazil 2 <1%
Australia 2 <1%
Norway 1 <1%
Ireland 1 <1%
China 1 <1%
Korea, Republic of 1 <1%
Netherlands 1 <1%
Other 4 2%
Unknown 210 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 48 20%
Student > Master 47 20%
Researcher 32 14%
Student > Doctoral Student 18 8%
Librarian 17 7%
Other 74 31%
Readers by discipline Count As %
Computer Science 60 25%
Medicine and Dentistry 58 25%
Unspecified 27 11%
Agricultural and Biological Sciences 21 9%
Social Sciences 12 5%
Other 58 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 62. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 November 2018.
All research outputs
#232,349
of 12,340,937 outputs
Outputs from Systematic Reviews
#34
of 985 outputs
Outputs of similar age
#4,438
of 192,908 outputs
Outputs of similar age from Systematic Reviews
#1
of 30 outputs
Altmetric has tracked 12,340,937 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 985 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.0. This one has done particularly well, scoring higher than 96% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 192,908 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.