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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Deep Learning-Based Method to Differentiate Neuromyelitis Optica Spectrum Disorder From Multiple Sclerosis
|
|---|---|
| Published in |
Frontiers in Neurology, November 2020
|
| DOI | 10.3389/fneur.2020.599042 |
| Pubmed ID | |
| Authors |
Hyunjin Kim, Youngin Lee, Yong-Hwan Kim, Young-Min Lim, Ji Sung Lee, Jincheol Woo, Su-Kyeong Jang, Yeo Jin Oh, Hye Weon Kim, Eun-Jae Lee, Dong-Wha Kang, Kwang-Kuk Kim |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 3 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Practitioners (doctors, other healthcare professionals) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 49 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Unspecified | 4 | 8% |
| Professor | 4 | 8% |
| Student > Bachelor | 4 | 8% |
| Student > Master | 4 | 8% |
| Student > Doctoral Student | 3 | 6% |
| Other | 11 | 22% |
| Unknown | 19 | 39% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 7 | 14% |
| Computer Science | 5 | 10% |
| Unspecified | 4 | 8% |
| Neuroscience | 4 | 8% |
| Engineering | 3 | 6% |
| Other | 4 | 8% |
| Unknown | 22 | 45% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 05 January 2021.
All research outputs
#2,873,710
of 23,267,128 outputs
Outputs from Frontiers in Neurology
#1,774
of 12,176 outputs
Outputs of similar age
#80,138
of 508,818 outputs
Outputs of similar age from Frontiers in Neurology
#184
of 602 outputs
Altmetric has tracked 23,267,128 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 12,176 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.3. This one has done well, scoring higher than 85% 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 508,818 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 602 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.