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Attention Score in Context
Title |
Prototyping a precision oncology 3.0 rapid learning platform
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Published in |
BMC Bioinformatics, September 2018
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DOI | 10.1186/s12859-018-2374-0 |
Pubmed ID | |
Authors |
Connor Sweetnam, Simone Mocellin, Michael Krauthammer, Nathaniel Knopf, Robert Baertsch, Jeff Shrager |
Abstract |
We describe a prototype implementation of a platform that could underlie a Precision Oncology Rapid Learning system. We describe the prototype platform, and examine some important issues and details. In the Appendix we provide a complete walk-through of the prototype platform. The design choices made in this implementation rest upon ten constitutive hypotheses, which, taken together, define a particular view of how a rapid learning medical platform might be defined, organized, and implemented. |
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.
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 25% |
France | 1 | 25% |
Canada | 1 | 25% |
Unknown | 1 | 25% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 50% |
Scientists | 2 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 28 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Bachelor | 4 | 14% |
Student > Master | 4 | 14% |
Other | 3 | 11% |
Researcher | 3 | 11% |
Student > Ph. D. Student | 1 | 4% |
Other | 3 | 11% |
Unknown | 10 | 36% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 7 | 25% |
Biochemistry, Genetics and Molecular Biology | 2 | 7% |
Agricultural and Biological Sciences | 2 | 7% |
Engineering | 2 | 7% |
Computer Science | 2 | 7% |
Other | 3 | 11% |
Unknown | 10 | 36% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 01 October 2018.
All research outputs
#15,019,263
of 23,105,443 outputs
Outputs from BMC Bioinformatics
#5,084
of 7,329 outputs
Outputs of similar age
#202,783
of 341,556 outputs
Outputs of similar age from BMC Bioinformatics
#70
of 107 outputs
Altmetric has tracked 23,105,443 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,329 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 26th percentile – i.e., 26% 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 341,556 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 107 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.