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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A diagnostic methodology for Alzheimer’s disease
|
|---|---|
| Published in |
Journal of Clinical Bioinformatics, April 2013
|
| DOI | 10.1186/2043-9113-3-9 |
| Pubmed ID | |
| Authors |
Wen-Chin Hsu, Christopher Denq, Su-Shing Chen |
| Abstract |
Like all other neurodegenerative diseases, Alzheimer's disease (AD) remains a very challenging and difficult problem for diagnosis and therapy. For many years, only historical, behavioral and psychiatric measures have been available to AD cases. Recently, a definitive diagnostic framework, using biomarkers and imaging, has been proposed. In this paper, we propose a promising diagnostic methodology for the framework. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 4% |
| Unknown | 22 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 30% |
| Student > Ph. D. Student | 4 | 17% |
| Student > Master | 3 | 13% |
| Student > Doctoral Student | 2 | 9% |
| Student > Bachelor | 2 | 9% |
| Other | 3 | 13% |
| Unknown | 2 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 4 | 17% |
| Biochemistry, Genetics and Molecular Biology | 3 | 13% |
| Medicine and Dentistry | 3 | 13% |
| Psychology | 3 | 13% |
| Agricultural and Biological Sciences | 2 | 9% |
| Other | 6 | 26% |
| Unknown | 2 | 9% |
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 26 April 2013.
All research outputs
#19,942,887
of 25,371,288 outputs
Outputs from Journal of Clinical Bioinformatics
#43
of 61 outputs
Outputs of similar age
#150,489
of 205,930 outputs
Outputs of similar age from Journal of Clinical Bioinformatics
#4
of 6 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 61 research outputs from this source. They receive a mean Attention Score of 3.1. This one is in the 29th percentile – i.e., 29% 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 205,930 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.