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Pinpointing needles in giant haystacks: use of text mining to reduce impractical screening workload in extremely large scoping reviews

Overview of attention for article published in Research Synthesis Methods, August 2013
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)

Mentioned by

twitter
22 tweeters

Citations

dimensions_citation
69 Dimensions

Readers on

mendeley
133 Mendeley
citeulike
5 CiteULike
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Title
Pinpointing needles in giant haystacks: use of text mining to reduce impractical screening workload in extremely large scoping reviews
Published in
Research Synthesis Methods, August 2013
DOI 10.1002/jrsm.1093
Pubmed ID
Authors

Ian Shemilt, Antonia Simon, Gareth J. Hollands, Theresa M. Marteau, David Ogilvie, Alison O'Mara-Eves, Michael P. Kelly, James Thomas

Abstract

In scoping reviews, boundaries of relevant evidence may be initially fuzzy, with refined conceptual understanding of interventions and their proposed mechanisms of action an intended output of the scoping process rather than its starting point. Electronic searches are therefore sensitive, often retrieving very large record sets that are impractical to screen in their entirety. This paper describes methods for applying and evaluating the use of text mining (TM) technologies to reduce impractical screening workload in reviews, using examples of two extremely large-scale scoping reviews of public health evidence (choice architecture (CA) and economic environment (EE)). Electronic searches retrieved >800,000 (CA) and >1 million (EE) records. TM technologies were used to prioritise records for manual screening. TM performance was measured prospectively. TM reduced manual screening workload by 90% (CA) and 88% (EE) compared with conventional screening (absolute reductions of ≈430 000 (CA) and ≈378 000 (EE) records). This study expands an emerging corpus of empirical evidence for the use of TM to expedite study selection in reviews. By reducing screening workload to manageable levels, TM made it possible to assemble and configure large, complex evidence bases that crossed research discipline boundaries. These methods are transferable to other scoping and systematic reviews incorporating conceptual development or explanatory dimensions. © 2013 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.

Twitter Demographics

The data shown below were collected from the profiles of 22 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 133 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 3%
United Kingdom 3 2%
Germany 1 <1%
Australia 1 <1%
Canada 1 <1%
Unknown 123 92%

Demographic breakdown

Readers by professional status Count As %
Student > Master 30 23%
Researcher 26 20%
Student > Ph. D. Student 23 17%
Librarian 15 11%
Student > Bachelor 7 5%
Other 32 24%
Readers by discipline Count As %
Medicine and Dentistry 30 23%
Computer Science 30 23%
Social Sciences 15 11%
Unspecified 13 10%
Agricultural and Biological Sciences 10 8%
Other 35 26%

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 01 August 2019.
All research outputs
#1,164,082
of 13,715,693 outputs
Outputs from Research Synthesis Methods
#51
of 266 outputs
Outputs of similar age
#15,252
of 159,417 outputs
Outputs of similar age from Research Synthesis Methods
#1
of 1 outputs
Altmetric has tracked 13,715,693 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 266 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.8. This one has done well, scoring higher than 80% 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 159,417 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 90% 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