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Glomerular disease search filters for Pubmed, Ovid Medline, and Embase: a development and validation study

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

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

blogs
1 blog
twitter
4 tweeters

Citations

dimensions_citation
11 Dimensions

Readers on

mendeley
41 Mendeley
citeulike
3 CiteULike
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Title
Glomerular disease search filters for Pubmed, Ovid Medline, and Embase: a development and validation study
Published in
BMC Medical Informatics and Decision Making, June 2012
DOI 10.1186/1472-6947-12-49
Pubmed ID
Authors

Ainslie M Hildebrand, Arthur V Iansavichus, Christopher WC Lee, R Brian Haynes, Nancy L Wilczynski, K Ann McKibbon, Michelle A Hladunewich, William F Clark, Daniel C Cattran, Amit X Garg

Abstract

Tools to enhance physician searches of Medline and other bibliographic databases have potential to improve the application of new knowledge in patient care. This is particularly true for articles about glomerular disease, which are published across multiple disciplines and are often difficult to track down. Our objective was to develop and test search filters for PubMed, Ovid Medline, and Embase that allow physicians to search within a subset of the database to retrieve articles relevant to glomerular disease.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 3 7%
Spain 2 5%
United States 1 2%
Unknown 35 85%

Demographic breakdown

Readers by professional status Count As %
Librarian 7 17%
Professor > Associate Professor 7 17%
Student > Ph. D. Student 5 12%
Researcher 5 12%
Professor 4 10%
Other 12 29%
Unknown 1 2%
Readers by discipline Count As %
Medicine and Dentistry 22 54%
Computer Science 5 12%
Social Sciences 3 7%
Nursing and Health Professions 2 5%
Biochemistry, Genetics and Molecular Biology 2 5%
Other 3 7%
Unknown 4 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 16 May 2016.
All research outputs
#1,632,951
of 12,409,138 outputs
Outputs from BMC Medical Informatics and Decision Making
#145
of 1,122 outputs
Outputs of similar age
#15,063
of 120,294 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#5
of 14 outputs
Altmetric has tracked 12,409,138 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,122 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 87% 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 120,294 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.