| Title |
A Novel Application of Unsupervised Machine Learning and Supervised Machine Learning-Derived Radiomics in Anterior Cruciate Ligament Rupture
|
|---|---|
| Published in |
Risk Management and Healthcare Policy, June 2021
|
| DOI | 10.2147/rmhp.s312330 |
| Pubmed ID | |
| Authors |
De-Sheng Chen, Tong-Fu Wang, Jia-Wang Zhu, Bo Zhu, Zeng-Liang Wang, Jian-Gang Cao, Cai-Hong Feng, Jun-Wei Zhao |
X Demographics
The data shown below were collected from the profiles of 4 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 | 2 | 50% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 17 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Unspecified | 1 | 6% |
| Other | 1 | 6% |
| Student > Doctoral Student | 1 | 6% |
| Student > Bachelor | 1 | 6% |
| Student > Ph. D. Student | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 11 | 65% |
| Readers by discipline | Count | As % |
|---|---|---|
| Arts and Humanities | 1 | 6% |
| Unspecified | 1 | 6% |
| Computer Science | 1 | 6% |
| Agricultural and Biological Sciences | 1 | 6% |
| Medicine and Dentistry | 1 | 6% |
| Other | 0 | 0% |
| Unknown | 12 | 71% |
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 24 June 2021.
All research outputs
#16,221,020
of 24,674,524 outputs
Outputs from Risk Management and Healthcare Policy
#354
of 706 outputs
Outputs of similar age
#251,858
of 438,558 outputs
Outputs of similar age from Risk Management and Healthcare Policy
#41
of 70 outputs
Altmetric has tracked 24,674,524 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 706 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.7. This one is in the 47th percentile – i.e., 47% of its peers scored the same or lower than it.
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 438,558 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 70 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.