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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Multiscale deep learning framework captures systemic immune features in lymph nodes predictive of triple negative breast cancer outcome in large‐scale studies
|
|---|---|
| Published in |
The Journal of Pathology, May 2023
|
| DOI | 10.1002/path.6088 |
| Pubmed ID | |
| Authors |
Gregory Verghese, Mengyuan Li, Fangfang Liu, Amit Lohan, Nikhil Cherian Kurian, Swati Meena, Patrycja Gazinska, Aekta Shah, Aasiyah Oozeer, Terry Chan, Mark Opdam, Sabine Linn, Cheryl Gillett, Elena Alberts, Thomas Hardiman, Samantha Jones, Selvam Thavaraj, J Louise Jones, Roberto Salgado, Sarah E Pinder, Swapnil Rane, Amit Sethi, Anita Grigoriadis |
X Demographics
The data shown below were collected from the profiles of 12 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 Kingdom | 3 | 25% |
| Brazil | 1 | 8% |
| Unknown | 8 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 8 | 67% |
| Practitioners (doctors, other healthcare professionals) | 2 | 17% |
| Scientists | 2 | 17% |
Mendeley readers
The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 23 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 3 | 13% |
| Student > Master | 2 | 9% |
| Student > Ph. D. Student | 2 | 9% |
| Lecturer | 1 | 4% |
| Professor | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 13 | 57% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 5 | 22% |
| Computer Science | 2 | 9% |
| Biochemistry, Genetics and Molecular Biology | 1 | 4% |
| Agricultural and Biological Sciences | 1 | 4% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 13 | 57% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1386. 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 13 July 2023.
All research outputs
#8,855
of 25,138,857 outputs
Outputs from The Journal of Pathology
#3
of 3,035 outputs
Outputs of similar age
#257
of 375,096 outputs
Outputs of similar age from The Journal of Pathology
#2
of 21 outputs
Altmetric has tracked 25,138,857 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,035 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.4. This one has done particularly well, scoring higher than 99% 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 375,096 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 99% of its contemporaries.
We're also able to compare this research output to 21 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 95% of its contemporaries.