| Title |
Hybrid 11C-MET PET/MRI Combined With "Machine Learning" in Glioma Diagnosis According to the Revised Glioma WHO Classification 2016.
|
|---|---|
| Published in |
Clinical Nuclear Medicine, March 2019
|
| DOI | 10.1097/rlu.0000000000002398 |
| Pubmed ID | |
| Authors |
Sied Kebir, Manuel Weber, Lazaros Lazaridis, Cornelius Deuschl, Teresa Schmidt, Christoph Mönninghoff, Kathy Keyvani, Lale Umutlu, Daniela Pierscianek, Michael Forsting, Ulrich Sure, Martin Stuschke, Christoph Kleinschnitz, Björn Scheffler, Patrick M Colletti, Domenico Rubello, Christoph Rischpler, Martin Glas |
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 % |
|---|---|---|
| Germany | 1 | 25% |
| Netherlands | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 3 | 75% |
| Members of the public | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 63 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 14 | 22% |
| Other | 6 | 10% |
| Student > Bachelor | 6 | 10% |
| Student > Ph. D. Student | 4 | 6% |
| Student > Postgraduate | 3 | 5% |
| Other | 9 | 14% |
| Unknown | 21 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 19 | 30% |
| Biochemistry, Genetics and Molecular Biology | 5 | 8% |
| Physics and Astronomy | 5 | 8% |
| Computer Science | 3 | 5% |
| Neuroscience | 3 | 5% |
| Other | 4 | 6% |
| Unknown | 24 | 38% |
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 19 September 2020.
All research outputs
#14,608,799
of 25,385,509 outputs
Outputs from Clinical Nuclear Medicine
#772
of 5,008 outputs
Outputs of similar age
#183,907
of 367,999 outputs
Outputs of similar age from Clinical Nuclear Medicine
#11
of 149 outputs
Altmetric has tracked 25,385,509 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,008 research outputs from this source. They receive a mean Attention Score of 1.5. This one has done well, scoring higher than 83% 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 367,999 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 149 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 91% of its contemporaries.