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Pneumonia burden in elderly patients: a classification algorithm using administrative data

Overview of attention for article published in BMC Infectious Diseases, November 2013
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1 tweeter

Citations

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Title
Pneumonia burden in elderly patients: a classification algorithm using administrative data
Published in
BMC Infectious Diseases, November 2013
DOI 10.1186/1471-2334-13-559
Pubmed ID
Authors

Silvia Cascini, Nera Agabiti, Raffaele Antonelli Incalzi, Luigi Pinnarelli, Flavia Mayer, Massimo Arcà, Danilo Fusco, Marina Davoli

Abstract

Pneumonia has traditionally been classified into two subtypes: community-acquired pneumonia (CAP) and nosocomial pneumonia (NP). Recently, a new entity has been defined, called healthcare-associated pneumonia (HCAP). Few studies have investigated the potential of population-based, electronic, healthcare databases to identify the incidences of these three subtypes of pneumonia. The aim of this study was to estimate the burden of the three subtypes of pneumonia in elderly patients (aged 65+ years) in a large region of central Italy.

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 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 2%
Unknown 45 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 20%
Student > Postgraduate 5 11%
Student > Bachelor 5 11%
Student > Ph. D. Student 4 9%
Student > Doctoral Student 4 9%
Other 11 24%
Unknown 8 17%
Readers by discipline Count As %
Medicine and Dentistry 25 54%
Agricultural and Biological Sciences 5 11%
Social Sciences 2 4%
Pharmacology, Toxicology and Pharmaceutical Science 2 4%
Computer Science 1 2%
Other 3 7%
Unknown 8 17%

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 27 November 2013.
All research outputs
#3,065,351
of 4,507,509 outputs
Outputs from BMC Infectious Diseases
#1,657
of 2,509 outputs
Outputs of similar age
#78,537
of 120,060 outputs
Outputs of similar age from BMC Infectious Diseases
#87
of 120 outputs
Altmetric has tracked 4,507,509 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,509 research outputs from this source. They receive a mean Attention Score of 2.7. This one is in the 22nd percentile – i.e., 22% 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 120,060 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 120 others from the same source and published within six weeks on either side of this one. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.