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Mendeley readers
Attention Score in Context
| Title |
Chinese Ischemic Stroke Subclassification
|
|---|---|
| Published in |
Frontiers in Neurology, January 2011
|
| DOI | 10.3389/fneur.2011.00006 |
| Pubmed ID | |
| Authors |
S. Gao, Y. J. Wang, A. D. Xu, Y. S. Li, D. Z. Wang |
| Abstract |
Accurate classification of stroke has significant impact on patient care and conduction of stroke clinical trials. The current systems such as TOAST, SSS-TOAST, Korean TOAST, and A-S-C-O have limitations. With the advent of new imaging technology, there is a need to have a more accurate stroke subclassification system. Chinese ischemic stroke subclassification (CISS) system is a new two step system aims at the etiology and then underlying mechanism of a stroke. The first step classify stroke into five categories: large artery atherosclerosis (LAA), including atherosclerosis of aortic arch and intra-/extracranial large arteries, cardiogenic stroke, penetrating artery disease, other etiology, and undetermined etiology. The second step is to further classify the underlying mechanism of ischemic stroke from the intracranial and extracranial LAA into the parent artery (plaque or thrombosis) occluding penetrating artery, artery-to-artery embolism, hypoperfusion/impaired emboli clearance, and multiple mechanisms. Although clinical validation of CISS is being planned, CISS is an innovative system that offers much more detailed information on the pathophysiology of a stroke. |
Mendeley readers
The data shown below were compiled from readership statistics for 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 1% |
| United States | 1 | 1% |
| Australia | 1 | 1% |
| South Africa | 1 | 1% |
| Unknown | 82 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 14 | 16% |
| Researcher | 12 | 14% |
| Other | 9 | 10% |
| Student > Master | 7 | 8% |
| Student > Doctoral Student | 6 | 7% |
| Other | 23 | 27% |
| Unknown | 15 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 35 | 41% |
| Neuroscience | 13 | 15% |
| Engineering | 7 | 8% |
| Agricultural and Biological Sciences | 5 | 6% |
| Nursing and Health Professions | 2 | 2% |
| Other | 5 | 6% |
| Unknown | 19 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 07 November 2014.
All research outputs
#7,175,598
of 22,684,168 outputs
Outputs from Frontiers in Neurology
#4,487
of 11,581 outputs
Outputs of similar age
#52,791
of 180,355 outputs
Outputs of similar age from Frontiers in Neurology
#16
of 47 outputs
Altmetric has tracked 22,684,168 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 11,581 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 gotten more attention than average, scoring higher than 60% 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 180,355 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 70% of its contemporaries.
We're also able to compare this research output to 47 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 65% of its contemporaries.