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Mendeley readers
Attention Score in Context
| Title |
A diagnostic algorithm combining clinical and molecular data distinguishes Kawasaki disease from other febrile illnesses
|
|---|---|
| Published in |
BMC Medicine, December 2011
|
| DOI | 10.1186/1741-7015-9-130 |
| Pubmed ID | |
| Authors |
Xuefeng B Ling, Kenneth Lau, John T Kanegaye, Zheng Pan, Sihua Peng, Jun Ji, Gigi Liu, Yuichiro Sato, Tom TS Yu, John C Whitin, James Schilling, Jane C Burns, Harvey J Cohen |
| Abstract |
Kawasaki disease is an acute vasculitis of infants and young children that is recognized through a constellation of clinical signs that can mimic other benign conditions of childhood. The etiology remains unknown and there is no specific laboratory-based test to identify patients with Kawasaki disease. Treatment to prevent the complication of coronary artery aneurysms is most effective if administered early in the course of the illness. We sought to develop a diagnostic algorithm to help clinicians distinguish Kawasaki disease patients from febrile controls to allow timely initiation of treatment. |
Mendeley readers
The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Indonesia | 1 | 2% |
| United Kingdom | 1 | 2% |
| Unknown | 54 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 14% |
| Student > Master | 8 | 14% |
| Student > Ph. D. Student | 7 | 13% |
| Student > Postgraduate | 5 | 9% |
| Professor | 4 | 7% |
| Other | 13 | 23% |
| Unknown | 11 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 26 | 46% |
| Biochemistry, Genetics and Molecular Biology | 5 | 9% |
| Agricultural and Biological Sciences | 5 | 9% |
| Computer Science | 2 | 4% |
| Unspecified | 1 | 2% |
| Other | 3 | 5% |
| Unknown | 14 | 25% |
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 08 December 2011.
All research outputs
#20,155,513
of 22,663,150 outputs
Outputs from BMC Medicine
#3,293
of 3,397 outputs
Outputs of similar age
#218,892
of 240,742 outputs
Outputs of similar age from BMC Medicine
#29
of 30 outputs
Altmetric has tracked 22,663,150 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,397 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 43.6. This one is in the 1st percentile – i.e., 1% 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 240,742 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.