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Transfer learning approach based on computed tomography images for predicting late xerostomia after radiotherapy in patients with oropharyngeal cancer

Overview of attention for article published in Frontiers in Medicine, September 2022
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1 X user

Citations

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Title
Transfer learning approach based on computed tomography images for predicting late xerostomia after radiotherapy in patients with oropharyngeal cancer
Published in
Frontiers in Medicine, September 2022
DOI 10.3389/fmed.2022.993395
Pubmed ID
Authors

Annarita Fanizzi, Giovanni Scognamillo, Alessandra Nestola, Santa Bambace, Samantha Bove, Maria Colomba Comes, Cristian Cristofaro, Vittorio Didonna, Alessia Di Rito, Angelo Errico, Loredana Palermo, Pasquale Tamborra, Michele Troiano, Salvatore Parisi, Rossella Villani, Alfredo Zito, Marco Lioce, Raffaella Massafra

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.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 1 13%
Student > Bachelor 1 13%
Unknown 6 75%
Readers by discipline Count As %
Computer Science 1 13%
Medicine and Dentistry 1 13%
Unknown 6 75%
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 10 October 2022.
All research outputs
#22,044,440
of 24,593,959 outputs
Outputs from Frontiers in Medicine
#5,954
of 6,736 outputs
Outputs of similar age
#359,709
of 426,507 outputs
Outputs of similar age from Frontiers in Medicine
#429
of 502 outputs
Altmetric has tracked 24,593,959 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,736 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.9. This one is in the 1st percentile – i.e., 1% 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 426,507 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 502 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.