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Studying the potential impact of automated document classification on scheduling a systematic review update

Overview of attention for article published in BMC Medical Informatics and Decision Making, April 2012
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

Mentioned by

twitter
5 X users
facebook
1 Facebook page

Citations

dimensions_citation
51 Dimensions

Readers on

mendeley
83 Mendeley
citeulike
2 CiteULike
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Title
Studying the potential impact of automated document classification on scheduling a systematic review update
Published in
BMC Medical Informatics and Decision Making, April 2012
DOI 10.1186/1472-6947-12-33
Pubmed ID
Authors

Aaron M Cohen, Kyle Ambert, Marian McDonagh

Abstract

Systematic Reviews (SRs) are an essential part of evidence-based medicine, providing support for clinical practice and policy on a wide range of medical topics. However, producing SRs is resource-intensive, and progress in the research they review leads to SRs becoming outdated, requiring updates. Although the question of how and when to update SRs has been studied, the best method for determining when to update is still unclear, necessitating further research.

Timeline

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X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 83 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 2%
Canada 1 1%
Unknown 80 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 16 19%
Student > Ph. D. Student 15 18%
Researcher 13 16%
Student > Bachelor 6 7%
Other 5 6%
Other 14 17%
Unknown 14 17%
Readers by discipline Count As %
Medicine and Dentistry 23 28%
Computer Science 22 27%
Agricultural and Biological Sciences 4 5%
Psychology 3 4%
Social Sciences 3 4%
Other 12 14%
Unknown 16 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 13 February 2013.
All research outputs
#6,245,826
of 22,664,644 outputs
Outputs from BMC Medical Informatics and Decision Making
#584
of 1,978 outputs
Outputs of similar age
#42,658
of 161,911 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#9
of 38 outputs
Altmetric has tracked 22,664,644 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 1,978 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 69% 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 161,911 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.