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Machine learning algorithms for systematic review: reducing workload in a preclinical review of animal studies and reducing human screening error

Overview of attention for article published in Systematic Reviews, January 2019
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)

Mentioned by

twitter
56 tweeters
reddit
1 Redditor

Citations

dimensions_citation
17 Dimensions

Readers on

mendeley
111 Mendeley
citeulike
1 CiteULike
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Title
Machine learning algorithms for systematic review: reducing workload in a preclinical review of animal studies and reducing human screening error
Published in
Systematic Reviews, January 2019
DOI 10.1186/s13643-019-0942-7
Pubmed ID
Authors

Alexandra Bannach-Brown, Piotr Przybyła, James Thomas, Andrew S. C. Rice, Sophia Ananiadou, Jing Liao, Malcolm Robert Macleod

Twitter Demographics

The data shown below were collected from the profiles of 56 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 111 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 111 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 25 23%
Student > Ph. D. Student 23 21%
Student > Bachelor 10 9%
Researcher 10 9%
Professor 5 5%
Other 20 18%
Unknown 18 16%
Readers by discipline Count As %
Computer Science 18 16%
Medicine and Dentistry 16 14%
Agricultural and Biological Sciences 10 9%
Business, Management and Accounting 9 8%
Social Sciences 6 5%
Other 26 23%
Unknown 26 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 34. 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 28 January 2020.
All research outputs
#651,708
of 15,879,709 outputs
Outputs from Systematic Reviews
#123
of 1,410 outputs
Outputs of similar age
#21,129
of 335,391 outputs
Outputs of similar age from Systematic Reviews
#1
of 1 outputs
Altmetric has tracked 15,879,709 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,410 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.5. This one has done particularly well, scoring higher than 91% 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 335,391 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 93% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them