| Title |
Machine learning reveals multimodal MRI patterns predictive of isocitrate dehydrogenase and 1p/19q status in diffuse low- and high-grade gliomas
|
|---|---|
| Published in |
Journal of Neuro-Oncology, January 2019
|
| DOI | 10.1007/s11060-019-03096-0 |
| Pubmed ID | |
| Authors |
Hao Zhou, Ken Chang, Harrison X. Bai, Bo Xiao, Chang Su, Wenya Linda Bi, Paul J. Zhang, Joeky T. Senders, Martin Vallières, Vasileios K. Kavouridis, Alessandro Boaro, Omar Arnaout, Li Yang, Raymond Y. Huang |
X Demographics
The data shown below were collected from the profiles of 44 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 | 13 | 30% |
| United Kingdom | 4 | 9% |
| Singapore | 1 | 2% |
| France | 1 | 2% |
| Mexico | 1 | 2% |
| Colombia | 1 | 2% |
| Malaysia | 1 | 2% |
| Turkey | 1 | 2% |
| Italy | 1 | 2% |
| Other | 3 | 7% |
| Unknown | 17 | 39% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 28 | 64% |
| Scientists | 10 | 23% |
| Practitioners (doctors, other healthcare professionals) | 4 | 9% |
| Science communicators (journalists, bloggers, editors) | 2 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 105 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 105 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 15 | 14% |
| Student > Bachelor | 11 | 10% |
| Student > Ph. D. Student | 10 | 10% |
| Student > Master | 10 | 10% |
| Student > Postgraduate | 9 | 9% |
| Other | 22 | 21% |
| Unknown | 28 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 25 | 24% |
| Computer Science | 9 | 9% |
| Biochemistry, Genetics and Molecular Biology | 8 | 8% |
| Engineering | 6 | 6% |
| Physics and Astronomy | 4 | 4% |
| Other | 13 | 12% |
| Unknown | 40 | 38% |
Attention Score in Context
This research output has an Altmetric Attention Score of 27. 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 20 August 2019.
All research outputs
#1,440,147
of 25,364,603 outputs
Outputs from Journal of Neuro-Oncology
#55
of 3,255 outputs
Outputs of similar age
#33,789
of 446,612 outputs
Outputs of similar age from Journal of Neuro-Oncology
#2
of 76 outputs
Altmetric has tracked 25,364,603 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,255 research outputs from this source. They receive a mean Attention Score of 4.4. This one has done particularly well, scoring higher than 98% 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 446,612 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 92% of its contemporaries.
We're also able to compare this research output to 76 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.