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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

  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

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1 policy source
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4 X users

Citations

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340 Dimensions

Readers on

mendeley
140 Mendeley
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1 CiteULike
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
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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 140 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 135 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 31 22%
Student > Ph. D. Student 23 16%
Other 13 9%
Student > Master 13 9%
Student > Doctoral Student 9 6%
Other 29 21%
Unknown 22 16%
Readers by discipline Count As %
Medicine and Dentistry 44 31%
Mathematics 22 16%
Psychology 7 5%
Agricultural and Biological Sciences 6 4%
Pharmacology, Toxicology and Pharmaceutical Science 5 4%
Other 26 19%
Unknown 30 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 August 2023.
All research outputs
#6,173,200
of 24,363,506 outputs
Outputs from BMC Medical Research Methodology
#865
of 2,165 outputs
Outputs of similar age
#48,670
of 199,020 outputs
Outputs of similar age from BMC Medical Research Methodology
#10
of 31 outputs
Altmetric has tracked 24,363,506 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 2,165 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.5. This one has gotten more attention than average, scoring higher than 59% 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 199,020 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 75% of its contemporaries.
We're also able to compare this research output to 31 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 70% of its contemporaries.