| Title |
Machine learning for the prediction of volume responsiveness in patients with oliguric acute kidney injury in critical care
|
|---|---|
| Published in |
Critical Care, April 2019
|
| DOI | 10.1186/s13054-019-2411-z |
| Pubmed ID | |
| Authors |
Zhongheng Zhang, Kwok M. Ho, Yucai Hong |
X Demographics
The data shown below were collected from the profiles of 95 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Mexico | 20 | 21% |
| United States | 5 | 5% |
| United Kingdom | 5 | 5% |
| Spain | 3 | 3% |
| Argentina | 2 | 2% |
| Russia | 2 | 2% |
| Colombia | 2 | 2% |
| Peru | 2 | 2% |
| El Salvador | 2 | 2% |
| Other | 14 | 15% |
| Unknown | 38 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 73 | 77% |
| Practitioners (doctors, other healthcare professionals) | 16 | 17% |
| Scientists | 5 | 5% |
| Science communicators (journalists, bloggers, editors) | 1 | 1% |
Mendeley readers
The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 62 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Doctoral Student | 3 | 5% |
| Student > Ph. D. Student | 2 | 3% |
| Researcher | 2 | 3% |
| Professor > Associate Professor | 2 | 3% |
| Student > Master | 1 | 2% |
| Other | 3 | 5% |
| Unknown | 49 | 79% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 4 | 6% |
| Computer Science | 3 | 5% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 2% |
| Biochemistry, Genetics and Molecular Biology | 1 | 2% |
| Chemical Engineering | 1 | 2% |
| Other | 4 | 6% |
| Unknown | 48 | 77% |
Attention Score in Context
This research output has an Altmetric Attention Score of 61. 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 31 March 2022.
All research outputs
#705,384
of 25,617,409 outputs
Outputs from Critical Care
#485
of 6,589 outputs
Outputs of similar age
#16,015
of 366,945 outputs
Outputs of similar age from Critical Care
#14
of 113 outputs
Altmetric has tracked 25,617,409 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,589 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.8. This one has done particularly well, scoring higher than 92% 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 366,945 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 113 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.