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Within a smoking-cessation program, what impact does genetic information on lung cancer need to have to demonstrate cost-effectiveness?

Overview of attention for article published in Cost Effectiveness and Resource Allocation, January 2010
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#8 of 120)
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

blogs
1 blog
twitter
1 tweeter

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
48 Mendeley
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Title
Within a smoking-cessation program, what impact does genetic information on lung cancer need to have to demonstrate cost-effectiveness?
Published in
Cost Effectiveness and Resource Allocation, January 2010
DOI 10.1186/1478-7547-8-18
Pubmed ID
Authors

Louisa G Gordon, Nicholas G Hirst, Robert P Young, Paul M Brown

Abstract

Many smoking-cessation programs and pharmaceutical aids demonstrate substantial health gains for a relatively low allocation of resources. Genetic information represents a type of individualized or personal feedback regarding the risk of developing lung cancer, and hence the potential benefits from stopping smoking, may motivate the person to remain smoke-free. The purpose of this study was to explore what the impact of a genetic test needs to have within a typical smoking-cessation program aimed at heavy smokers in order to be cost-effective.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Unknown 47 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 15 31%
Researcher 6 13%
Student > Bachelor 5 10%
Student > Ph. D. Student 4 8%
Student > Doctoral Student 3 6%
Other 10 21%
Unknown 5 10%
Readers by discipline Count As %
Medicine and Dentistry 14 29%
Social Sciences 7 15%
Psychology 7 15%
Agricultural and Biological Sciences 3 6%
Economics, Econometrics and Finance 3 6%
Other 6 13%
Unknown 8 17%

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 17 June 2013.
All research outputs
#684,058
of 6,228,985 outputs
Outputs from Cost Effectiveness and Resource Allocation
#8
of 120 outputs
Outputs of similar age
#13,291
of 98,512 outputs
Outputs of similar age from Cost Effectiveness and Resource Allocation
#1
of 9 outputs
Altmetric has tracked 6,228,985 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 120 research outputs from this source. They receive a mean Attention Score of 2.9. This one has done particularly well, scoring higher than 93% 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 98,512 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 86% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them