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A graphical tool for locating inconsistency in network meta-analyses

Overview of attention for article published in BMC Medical Research Methodology, March 2013
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
190 Dimensions

Readers on

mendeley
102 Mendeley
citeulike
1 CiteULike

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Summary Twitter Dimensions citations
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Title
A graphical tool for locating inconsistency in network meta-analyses
Published in
BMC Medical Research Methodology, March 2013
DOI 10.1186/1471-2288-13-35
Pubmed ID
23496991
Authors

Ulrike Krahn, Harald Binder, Jochem König

Abstract

In network meta-analyses, several treatments can be compared by connecting evidence from clinical trials that have investigated two or more treatments. The resulting trial network allows estimating the relative effects of all pairs of treatments taking indirect evidence into account. For a valid analysis of the network, consistent information from different pathways is assumed. Consistency can be checked by contrasting effect estimates from direct comparisons with the evidence of the remaining network. Unfortunately, one deviating direct comparison may have side effects on the network estimates of others, thus producing hot spots of inconsistency.

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Twitter Demographics

The data shown below were collected from the profiles of 3 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 102 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 <1%
Netherlands 1 <1%
France 1 <1%
United Kingdom 1 <1%
Denmark 1 <1%
Unknown 97 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 23 23%
Student > Ph. D. Student 21 21%
Other 9 9%
Student > Master 9 9%
Professor > Associate Professor 8 8%
Other 25 25%
Unknown 7 7%
Readers by discipline Count As %
Medicine and Dentistry 34 33%
Mathematics 20 20%
Pharmacology, Toxicology and Pharmaceutical Science 5 5%
Agricultural and Biological Sciences 5 5%
Psychology 4 4%
Other 20 20%
Unknown 14 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 17 January 2015.
All research outputs
#6,352,414
of 11,344,222 outputs
Outputs from BMC Medical Research Methodology
#562
of 959 outputs
Outputs of similar age
#57,523
of 128,542 outputs
Outputs of similar age from BMC Medical Research Methodology
#22
of 36 outputs
Altmetric has tracked 11,344,222 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 959 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.8. This one is in the 39th percentile – i.e., 39% 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 128,542 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 54% of its contemporaries.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.

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