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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Early Prediction of Mortality, Severity, and Length of Stay in the Intensive Care Unit of Sepsis Patients Based on Sepsis 3.0 by Machine Learning Models
|
|---|---|
| Published in |
Frontiers in Medicine, June 2021
|
| DOI | 10.3389/fmed.2021.664966 |
| Pubmed ID | |
| Authors |
Longxiang Su, Zheng Xu, Fengxiang Chang, Yingying Ma, Shengjun Liu, Huizhen Jiang, Hao Wang, Dongkai Li, Huan Chen, Xiang Zhou, Na Hong, Weiguo Zhu, Yun Long |
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 % |
|---|---|---|
| Italy | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 73 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 11% |
| Student > Master | 6 | 8% |
| Student > Bachelor | 4 | 5% |
| Researcher | 4 | 5% |
| Other | 4 | 5% |
| Other | 10 | 14% |
| Unknown | 37 | 51% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 12 | 16% |
| Computer Science | 8 | 11% |
| Engineering | 7 | 10% |
| Nursing and Health Professions | 3 | 4% |
| Business, Management and Accounting | 1 | 1% |
| Other | 5 | 7% |
| Unknown | 37 | 51% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 27 July 2021.
All research outputs
#15,025,659
of 23,310,485 outputs
Outputs from Frontiers in Medicine
#2,857
of 5,969 outputs
Outputs of similar age
#236,372
of 442,221 outputs
Outputs of similar age from Frontiers in Medicine
#192
of 415 outputs
Altmetric has tracked 23,310,485 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,969 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 gotten more attention than average, scoring higher than 51% 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 442,221 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 415 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 53% of its contemporaries.