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Attention Score in Context
Title |
Advancing NSCLC pathological subtype prediction with interpretable machine learning: a comprehensive radiomics-based approach
|
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Published in |
Frontiers in Medicine, May 2024
|
DOI | 10.3389/fmed.2024.1413990 |
Pubmed ID | |
Authors |
Bingling Kuang, Jingxuan Zhang, Mingqi Zhang, Haoming Xia, Guangliang Qiang, Jiangyu Zhang |
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 20 May 2024.
All research outputs
#23,302,049
of 25,967,142 outputs
Outputs from Frontiers in Medicine
#6,548
of 7,373 outputs
Outputs of similar age
#117,218
of 146,454 outputs
Outputs of similar age from Frontiers in Medicine
#42
of 52 outputs
Altmetric has tracked 25,967,142 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 7,373 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.7. 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 146,454 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 52 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.