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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A Novel Volume-Age-KPS (VAK) Glioblastoma Classification Identifies a Prognostic Cognate microRNA-Gene Signature
|
|---|---|
| Published in |
PLOS ONE, August 2012
|
| DOI | 10.1371/journal.pone.0041522 |
| Pubmed ID | |
| Authors |
Pascal O. Zinn, Pratheesh Sathyan, Bhanu Mahajan, John Bruyere, Monika Hegi, Sadhan Majumder, Rivka R. Colen |
| Abstract |
Several studies have established Glioblastoma Multiforme (GBM) prognostic and predictive models based on age and Karnofsky Performance Status (KPS), while very few studies evaluated the prognostic and predictive significance of preoperative MR-imaging. However, to date, there is no simple preoperative GBM classification that also correlates with a highly prognostic genomic signature. Thus, we present for the first time a biologically relevant, and clinically applicable tumor Volume, patient Age, and KPS (VAK) GBM classification that can easily and non-invasively be determined upon patient admission. |
X Demographics
The data shown below were collected from the profiles of 2 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 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
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 % |
|---|---|---|
| United States | 2 | 2% |
| Korea, Republic of | 1 | 1% |
| Netherlands | 1 | 1% |
| Brazil | 1 | 1% |
| Unknown | 81 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 17 | 20% |
| Student > Ph. D. Student | 13 | 15% |
| Student > Master | 8 | 9% |
| Student > Bachelor | 8 | 9% |
| Professor > Associate Professor | 7 | 8% |
| Other | 21 | 24% |
| Unknown | 12 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 35 | 41% |
| Computer Science | 6 | 7% |
| Agricultural and Biological Sciences | 6 | 7% |
| Biochemistry, Genetics and Molecular Biology | 6 | 7% |
| Engineering | 5 | 6% |
| Other | 11 | 13% |
| Unknown | 17 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 09 August 2022.
All research outputs
#4,075,044
of 23,053,169 outputs
Outputs from PLOS ONE
#59,160
of 196,586 outputs
Outputs of similar age
#28,218
of 165,396 outputs
Outputs of similar age from PLOS ONE
#845
of 4,048 outputs
Altmetric has tracked 23,053,169 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 196,586 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.2. This one has gotten more attention than average, scoring higher than 69% 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 165,396 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 82% of its contemporaries.
We're also able to compare this research output to 4,048 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.