| Title |
Features for Predicting Absorbable Pulmonary Solid Nodules as Depicted on Thin-Section Computed Tomography
|
|---|---|
| Published in |
Journal of Inflammation Research, July 2021
|
| DOI | 10.2147/jir.s318125 |
| Pubmed ID | |
| Authors |
Rui-Yu Lin, Fa-Jin Lv, Bin-Jie Fu, Wang-Jia Li, Zhang-Rui Liang, Zhi-Gang Chu |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 18 October 2022.
All research outputs
#4,349,370
of 23,548,905 outputs
Outputs from Journal of Inflammation Research
#104
of 843 outputs
Outputs of similar age
#100,826
of 443,735 outputs
Outputs of similar age from Journal of Inflammation Research
#8
of 84 outputs
Altmetric has tracked 23,548,905 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 843 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.3. This one has done well, scoring higher than 86% 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 443,735 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 84 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.