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Uncovering the burden of hidradenitis suppurativa misdiagnosis and underdiagnosis: a machine learning approach

Overview of attention for article published in Frontiers in Medical Technology, March 2024
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#14 of 298)
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

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2 news outlets
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3 X users

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1 Mendeley
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Title
Uncovering the burden of hidradenitis suppurativa misdiagnosis and underdiagnosis: a machine learning approach
Published in
Frontiers in Medical Technology, March 2024
DOI 10.3389/fmedt.2024.1200400
Pubmed ID
Authors

Joslyn Kirby, Katherine Kim, Marko Zivkovic, Siwei Wang, Vishvas Garg, Akash Danavar, Chao Li, Naijun Chen, Amit Garg

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 18. 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 12 May 2024.
All research outputs
#2,066,746
of 25,888,937 outputs
Outputs from Frontiers in Medical Technology
#14
of 298 outputs
Outputs of similar age
#27,151
of 318,216 outputs
Outputs of similar age from Frontiers in Medical Technology
#1
of 17 outputs
Altmetric has tracked 25,888,937 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 298 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one has done particularly well, scoring higher than 95% 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 318,216 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 91% of its contemporaries.
We're also able to compare this research output to 17 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 94% of its contemporaries.