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Prognostic factors and predictive model construction in patients with non-small cell lung cancer: a retrospective study

Overview of attention for article published in Frontiers in oncology, May 2024
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Title
Prognostic factors and predictive model construction in patients with non-small cell lung cancer: a retrospective study
Published in
Frontiers in oncology, May 2024
DOI 10.3389/fonc.2024.1378135
Pubmed ID
Authors

Shixin Ma, Lunqing Wang

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 June 2024.
All research outputs
#23,472,172
of 26,147,626 outputs
Outputs from Frontiers in oncology
#16,345
of 22,908 outputs
Outputs of similar age
#181,668
of 226,638 outputs
Outputs of similar age from Frontiers in oncology
#89
of 131 outputs
Altmetric has tracked 26,147,626 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 22,908 research outputs from this source. They receive a mean Attention Score of 3.1. 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 226,638 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 131 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.