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The Ontology of Vaccine Adverse Events (OVAE) and its usage in representing and analyzing adverse events associated with US-licensed human vaccines

Overview of attention for article published in Journal of Biomedical Semantics, January 2013
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  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
1 tweeter
wikipedia
1 Wikipedia page

Citations

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28 Dimensions

Readers on

mendeley
10 Mendeley
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Title
The Ontology of Vaccine Adverse Events (OVAE) and its usage in representing and analyzing adverse events associated with US-licensed human vaccines
Published in
Journal of Biomedical Semantics, January 2013
DOI 10.1186/2041-1480-4-40
Pubmed ID
Authors

Erica Marcos, Bin Zhao, Yongqun He

Abstract

Licensed human vaccines can induce various adverse events (AE) in vaccinated patients. Due to the involvement of the whole immune system and complex immunological reactions after vaccination, it is difficult to identify the relations among vaccines, adverse events, and human populations in different age groups. Many known vaccine adverse events (VAEs) have been recorded in the package inserts of US-licensed commercial vaccine products. To better represent and analyze VAEs, we developed the Ontology of Vaccine Adverse Events (OVAE) as an extension of the Ontology of Adverse Events (OAE) and the Vaccine Ontology (VO).

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

Geographical breakdown

Country Count As %
Mexico 1 10%
Unknown 9 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 50%
Researcher 1 10%
Other 1 10%
Professor > Associate Professor 1 10%
Student > Postgraduate 1 10%
Other 0 0%
Unknown 1 10%
Readers by discipline Count As %
Computer Science 8 80%
Agricultural and Biological Sciences 1 10%
Unknown 1 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 19 February 2020.
All research outputs
#4,605,493
of 15,670,808 outputs
Outputs from Journal of Biomedical Semantics
#118
of 344 outputs
Outputs of similar age
#70,149
of 258,165 outputs
Outputs of similar age from Journal of Biomedical Semantics
#20
of 32 outputs
Altmetric has tracked 15,670,808 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 344 research outputs from this source. They receive a mean Attention Score of 4.5. This one has gotten more attention than average, scoring higher than 62% 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 258,165 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 71% of its contemporaries.
We're also able to compare this research output to 32 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.