| Title |
Development of a quantitative segmentation model to assess the effect of comorbidity on patients with COVID-19
|
|---|---|
| Published in |
European Journal of Medical Research, October 2020
|
| DOI | 10.1186/s40001-020-00450-1 |
| Pubmed ID | |
| Authors |
Cui Zhang, Guangzhao Yang, Chunxian Cai, Zhihua Xu, Hai Wu, Youmin Guo, Zongyu Xie, Hengfeng Shi, Guohua Cheng, Jian Wang |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 67% |
| Unknown | 2 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 67% |
| Practitioners (doctors, other healthcare professionals) | 2 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 99 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 99 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 12 | 12% |
| Student > Bachelor | 12 | 12% |
| Researcher | 10 | 10% |
| Other | 5 | 5% |
| Student > Postgraduate | 5 | 5% |
| Other | 20 | 20% |
| Unknown | 35 | 35% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 20 | 20% |
| Nursing and Health Professions | 8 | 8% |
| Computer Science | 7 | 7% |
| Engineering | 5 | 5% |
| Biochemistry, Genetics and Molecular Biology | 3 | 3% |
| Other | 19 | 19% |
| Unknown | 37 | 37% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 18 February 2021.
All research outputs
#14,611,205
of 25,387,668 outputs
Outputs from European Journal of Medical Research
#314
of 924 outputs
Outputs of similar age
#209,929
of 435,995 outputs
Outputs of similar age from European Journal of Medical Research
#5
of 18 outputs
Altmetric has tracked 25,387,668 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 924 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.8. This one has gotten more attention than average, scoring higher than 65% 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 435,995 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 50% of its contemporaries.
We're also able to compare this research output to 18 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 72% of its contemporaries.