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Machine learning classification of mediastinal lymph node metastasis in NSCLC: a multicentre study in a Western European patient population

Overview of attention for article published in EJNMMI Physics, September 2022
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Mentioned by

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1 X user
reddit
1 Redditor

Citations

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3 Dimensions

Readers on

mendeley
12 Mendeley
Title
Machine learning classification of mediastinal lymph node metastasis in NSCLC: a multicentre study in a Western European patient population
Published in
EJNMMI Physics, September 2022
DOI 10.1186/s40658-022-00494-8
Pubmed ID
Authors

Sara S. A. Laros, Dennis Dieckens, Stephan P. Blazis, Johannes A. van der Heide

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 2 17%
Student > Bachelor 1 8%
Researcher 1 8%
Lecturer 1 8%
Student > Doctoral Student 1 8%
Other 0 0%
Unknown 6 50%
Readers by discipline Count As %
Unspecified 2 17%
Medicine and Dentistry 2 17%
Computer Science 1 8%
Business, Management and Accounting 1 8%
Unknown 6 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 29 September 2022.
All research outputs
#19,955,104
of 24,525,534 outputs
Outputs from EJNMMI Physics
#123
of 199 outputs
Outputs of similar age
#312,196
of 426,571 outputs
Outputs of similar age from EJNMMI Physics
#10
of 14 outputs
Altmetric has tracked 24,525,534 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 199 research outputs from this source. They receive a mean Attention Score of 2.5. This one is in the 28th percentile – i.e., 28% of its peers scored the same or lower than it.
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 426,571 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.