| Title |
Prognostic Factors for COVID-19 Pneumonia Progression to Severe Symptoms Based on Earlier Clinical Features: A Retrospective Analysis
|
|---|---|
| Published in |
Frontiers in Medicine, October 2020
|
| DOI | 10.3389/fmed.2020.557453 |
| Pubmed ID | |
| Authors |
Huang Huang, Shuijiang Cai, Yueping Li, Youxia Li, Yinqiang Fan, Linghua Li, Chunliang Lei, Xiaoping Tang, Fengyu Hu, Feng Li, Xilong Deng |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 120 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 120 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 19 | 16% |
| Researcher | 18 | 15% |
| Other | 13 | 11% |
| Student > Master | 13 | 11% |
| Student > Ph. D. Student | 7 | 6% |
| Other | 16 | 13% |
| Unknown | 34 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 38 | 32% |
| Engineering | 9 | 8% |
| Pharmacology, Toxicology and Pharmaceutical Science | 7 | 6% |
| Computer Science | 5 | 4% |
| Nursing and Health Professions | 4 | 3% |
| Other | 16 | 13% |
| Unknown | 41 | 34% |
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 09 December 2020.
All research outputs
#2,889,800
of 23,248,929 outputs
Outputs from Frontiers in Medicine
#711
of 5,937 outputs
Outputs of similar age
#76,346
of 412,945 outputs
Outputs of similar age from Frontiers in Medicine
#32
of 226 outputs
Altmetric has tracked 23,248,929 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 5,937 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.5. This one has done well, scoring higher than 87% 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 412,945 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 81% of its contemporaries.
We're also able to compare this research output to 226 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.