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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care
|
|---|---|
| Published in |
Critical Care, August 2021
|
| DOI | 10.1186/s13054-021-03724-0 |
| Pubmed ID | |
| Authors |
Junzi Dong, Ting Feng, Binod Thapa-Chhetry, Byung Gu Cho, Tunu Shum, David P. Inwald, Christopher J. L. Newth, Vinay U. Vaidya |
X Demographics
The data shown below were collected from the profiles of 20 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 Kingdom | 2 | 10% |
| Malaysia | 1 | 5% |
| France | 1 | 5% |
| Mexico | 1 | 5% |
| Germany | 1 | 5% |
| Italy | 1 | 5% |
| Belgium | 1 | 5% |
| United States | 1 | 5% |
| Unknown | 11 | 55% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 15 | 75% |
| Scientists | 2 | 10% |
| Practitioners (doctors, other healthcare professionals) | 2 | 10% |
| Science communicators (journalists, bloggers, editors) | 1 | 5% |
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 % |
|---|---|---|
| Unknown | 86 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 13 | 15% |
| Student > Ph. D. Student | 8 | 9% |
| Student > Bachelor | 6 | 7% |
| Student > Master | 5 | 6% |
| Professor | 4 | 5% |
| Other | 11 | 13% |
| Unknown | 39 | 45% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 25 | 29% |
| Engineering | 5 | 6% |
| Nursing and Health Professions | 3 | 3% |
| Computer Science | 3 | 3% |
| Agricultural and Biological Sciences | 2 | 2% |
| Other | 6 | 7% |
| Unknown | 42 | 49% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 08 August 2022.
All research outputs
#3,801,337
of 25,392,582 outputs
Outputs from Critical Care
#2,884
of 6,555 outputs
Outputs of similar age
#85,420
of 436,785 outputs
Outputs of similar age from Critical Care
#73
of 109 outputs
Altmetric has tracked 25,392,582 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,555 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 gotten more attention than average, scoring higher than 55% 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 436,785 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 80% of its contemporaries.
We're also able to compare this research output to 109 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.