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Machine learning-based prediction model for the efficacy and safety of statins

Overview of attention for article published in Frontiers in Pharmacology, July 2024
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Title
Machine learning-based prediction model for the efficacy and safety of statins
Published in
Frontiers in Pharmacology, July 2024
DOI 10.3389/fphar.2024.1334929
Pubmed ID
Authors

Yu Xiong, Xiaoyang Liu, Qing Wang, Li Zhao, Xudong Kong, Chunhe Da, Zuohuan Meng, Leilei Qu, Qinfang Xia, Lihong Liu, Pengmei Li

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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 July 2024.
All research outputs
#23,707,648
of 26,390,482 outputs
Outputs from Frontiers in Pharmacology
#13,091
of 20,367 outputs
Outputs of similar age
#103,788
of 132,546 outputs
Outputs of similar age from Frontiers in Pharmacology
#57
of 201 outputs
Altmetric has tracked 26,390,482 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 20,367 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. 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 132,546 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 201 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.