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Developing optimal search strategies for detecting clinically sound and relevant causation studies in EMBASE

Overview of attention for article published in BMC Medical Informatics and Decision Making, March 2005
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About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

peer_reviews
1 peer review site

Citations

dimensions_citation
53 Dimensions

Readers on

mendeley
78 Mendeley
citeulike
2 CiteULike
connotea
2 Connotea
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Title
Developing optimal search strategies for detecting clinically sound and relevant causation studies in EMBASE
Published in
BMC Medical Informatics and Decision Making, March 2005
DOI 10.1186/1472-6947-5-8
Pubmed ID
Authors

R Brian Haynes, Monika Kastner, Nancy L Wilczynski

Abstract

Evaluating the existence and strength of an association between a putative cause and adverse clinical outcome is complex and best done by assessing all available evidence. With the increasing burden of chronic disease, greater time demands on health professionals, and the explosion of information, effective retrieval of best evidence has become both more important and more difficult. Optimal search retrieval can be hampered by a number of obstacles, especially poor search strategies, but using empirically tested methodological search filters can enhance the accuracy of searches for sound evidence concerning etiology. Although such filters have previously been developed for studies of relevance to causation in MEDLINE, no empirically tested search strategy exists for EMBASE.

Mendeley readers

The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 4 5%
Brazil 2 3%
United Kingdom 2 3%
Peru 1 1%
Canada 1 1%
Nigeria 1 1%
United States 1 1%
Unknown 66 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 22%
Librarian 10 13%
Student > Master 9 12%
Student > Doctoral Student 7 9%
Professor > Associate Professor 7 9%
Other 21 27%
Unknown 7 9%
Readers by discipline Count As %
Medicine and Dentistry 38 49%
Nursing and Health Professions 6 8%
Psychology 4 5%
Computer Science 3 4%
Agricultural and Biological Sciences 2 3%
Other 11 14%
Unknown 14 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 December 2014.
All research outputs
#14,312,523
of 21,321,193 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,252
of 1,867 outputs
Outputs of similar age
#200,873
of 344,670 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#90
of 123 outputs
Altmetric has tracked 21,321,193 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,867 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one is in the 23rd percentile – i.e., 23% of its peers scored the same or lower than it.
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 344,670 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 123 others from the same source and published within six weeks on either side of this one. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.