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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Mining multi-site clinical data to develop machine learning MRI biomarkers: application to neonatal hypoxic ischemic encephalopathy
|
|---|---|
| Published in |
Journal of Translational Medicine, November 2019
|
| DOI | 10.1186/s12967-019-2119-5 |
| Pubmed ID | |
| Authors |
Rebecca J. Weiss, Sara V. Bates, Ya’nan Song, Yue Zhang, Emily M. Herzberg, Yih-Chieh Chen, Maryann Gong, Isabel Chien, Lily Zhang, Shawn N. Murphy, Randy L. Gollub, P. Ellen Grant, Yangming Ou |
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 | 1 | 25% |
| Netherlands | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Practitioners (doctors, other healthcare professionals) | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 71 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 10 | 14% |
| Student > Bachelor | 10 | 14% |
| Student > Ph. D. Student | 9 | 13% |
| Student > Master | 8 | 11% |
| Student > Doctoral Student | 6 | 8% |
| Other | 13 | 18% |
| Unknown | 15 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 21 | 30% |
| Neuroscience | 6 | 8% |
| Computer Science | 6 | 8% |
| Nursing and Health Professions | 5 | 7% |
| Engineering | 4 | 6% |
| Other | 10 | 14% |
| Unknown | 19 | 27% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 22 December 2019.
All research outputs
#13,182,073
of 23,340,595 outputs
Outputs from Journal of Translational Medicine
#1,513
of 4,126 outputs
Outputs of similar age
#208,170
of 458,989 outputs
Outputs of similar age from Journal of Translational Medicine
#27
of 65 outputs
Altmetric has tracked 23,340,595 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,126 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.5. This one has gotten more attention than average, scoring higher than 62% 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 458,989 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.
We're also able to compare this research output to 65 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 56% of its contemporaries.