Title |
Neural network and logistic regression diagnostic prediction models for giant cell arteritis: development and validation
|
---|---|
Published in |
Clinical Ophthalmology, February 2019
|
DOI | 10.2147/opth.s193460 |
Pubmed ID | |
Authors |
Edsel B Ing, Neil R Miller, Angeline Nguyen, Wanhua Su, Lulu L C D Bursztyn, Meredith Poole, Vinay Kansal, Andrew Toren, Dana Albreki, Jack G Mouhanna, Alla Muladzanov, Mikaël Bernier, Mark Gans, Dongho Lee, Colten Wendel, Claire Sheldon, Marc Shields, Lorne Bellan, Matthew Lee-Wing, Yasaman Mohadjer, Navdeep Nijhawan, Felix Tyndel, Arun N E Sundaram, Martin W ten Hove, John J Chen, Amadeo R Rodriguez, Angela Hu, Nader Khalidi, Royce Ing, Samuel W K Wong, Nurhan Torun |
X Demographics
The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
Canada | 1 | 14% |
France | 1 | 14% |
United States | 1 | 14% |
Korea, Republic of | 1 | 14% |
United Kingdom | 1 | 14% |
Unknown | 2 | 29% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 4 | 57% |
Scientists | 2 | 29% |
Practitioners (doctors, other healthcare professionals) | 1 | 14% |
Mendeley readers
The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 45 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Bachelor | 9 | 20% |
Student > Ph. D. Student | 7 | 16% |
Professor > Associate Professor | 4 | 9% |
Researcher | 3 | 7% |
Student > Doctoral Student | 2 | 4% |
Other | 7 | 16% |
Unknown | 13 | 29% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 16 | 36% |
Computer Science | 3 | 7% |
Engineering | 3 | 7% |
Nursing and Health Professions | 2 | 4% |
Biochemistry, Genetics and Molecular Biology | 2 | 4% |
Other | 4 | 9% |
Unknown | 15 | 33% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 29 May 2019.
All research outputs
#7,051,839
of 25,385,509 outputs
Outputs from Clinical Ophthalmology
#581
of 3,714 outputs
Outputs of similar age
#139,500
of 447,265 outputs
Outputs of similar age from Clinical Ophthalmology
#9
of 53 outputs
Altmetric has tracked 25,385,509 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 3,714 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 83% 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 447,265 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 68% of its contemporaries.
We're also able to compare this research output to 53 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.