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Bayesian predictors of very poor health related quality of life and mortality in patients with COPD

Overview of attention for article published in BMC Medical Informatics and Decision Making, March 2013
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  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
15 Dimensions

Readers on

mendeley
52 Mendeley
citeulike
1 CiteULike
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Title
Bayesian predictors of very poor health related quality of life and mortality in patients with COPD
Published in
BMC Medical Informatics and Decision Making, March 2013
DOI 10.1186/1472-6947-13-34
Pubmed ID
Authors

Olli-Pekka Ryynänen, Erkki J Soini, Ari Lindqvist, Maritta Kilpeläinen, Tarja Laitinen

Abstract

Chronic obstructive pulmonary disease (COPD) is associated with increased mortality and poor health-related quality of life (HRQoL) compared with the general population. The objective of this study was to identify clinical characteristics which predict mortality and very poor HRQoL among the COPD population and to develop a Bayesian prediction model.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
United States 1 2%
Unknown 50 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 14 27%
Student > Ph. D. Student 10 19%
Student > Bachelor 7 13%
Researcher 6 12%
Professor > Associate Professor 4 8%
Other 9 17%
Unknown 2 4%
Readers by discipline Count As %
Medicine and Dentistry 21 40%
Nursing and Health Professions 6 12%
Computer Science 4 8%
Psychology 3 6%
Engineering 2 4%
Other 8 15%
Unknown 8 15%

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 12 March 2013.
All research outputs
#6,231,217
of 10,502,506 outputs
Outputs from BMC Medical Informatics and Decision Making
#736
of 1,044 outputs
Outputs of similar age
#65,305
of 125,327 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#37
of 44 outputs
Altmetric has tracked 10,502,506 research outputs across all sources so far. This one is in the 24th percentile – i.e., 24% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,044 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 19th percentile – i.e., 19% 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 125,327 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 44 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.