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Clinical and molecular feature-based nomogram model for predicting benefit from bevacizumab combined with first-generation EGFR-tyrosine kinase inhibitor (TKI) in EGFR-mutant advanced NSCLC

Overview of attention for article published in BMC Medicine, October 2021
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (56th percentile)

Mentioned by

twitter
4 tweeters

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
4 Mendeley
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Title
Clinical and molecular feature-based nomogram model for predicting benefit from bevacizumab combined with first-generation EGFR-tyrosine kinase inhibitor (TKI) in EGFR-mutant advanced NSCLC
Published in
BMC Medicine, October 2021
DOI 10.1186/s12916-021-02118-x
Pubmed ID
Authors

Yongchang Zhang, Liang Zeng, Xiangyu Zhang, Yizhi Li, Lingli Liu, Qinqin Xu, Haiyan Yang, Wenjuan Jiang, Analyn Lizaso, Luting Qiu, Ting Hou, Jun Liu, Ling Peng, Nong Yang

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 50%
Lecturer > Senior Lecturer 1 25%
Unknown 1 25%
Readers by discipline Count As %
Medicine and Dentistry 2 50%
Sports and Recreations 1 25%
Unknown 1 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 22 October 2021.
All research outputs
#11,793,557
of 20,886,305 outputs
Outputs from BMC Medicine
#2,492
of 3,068 outputs
Outputs of similar age
#177,412
of 420,798 outputs
Outputs of similar age from BMC Medicine
#224
of 306 outputs
Altmetric has tracked 20,886,305 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,068 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 39.9. This one is in the 18th percentile – i.e., 18% 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 420,798 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.
We're also able to compare this research output to 306 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.