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X Demographics
Mendeley readers
Attention Score in Context
| Title |
DeepCUBIT: Predicting Lymphovascular Invasion or Pathological Lymph Node Involvement of Clinical T1 Stage Non-Small Cell Lung Cancer on Chest CT Scan Using Deep Cubical Nodule Transfer Learning Algorithm
|
|---|---|
| Published in |
Frontiers in oncology, July 2021
|
| DOI | 10.3389/fonc.2021.661244 |
| Pubmed ID | |
| Authors |
Kyongmin Sarah Beck, Bomi Gil, Sae Jung Na, Ji Hyung Hong, Sang Hoon Chun, Ho Jung An, Jae Jun Kim, Soon Auck Hong, Bora Lee, Won Sang Shim, Sungsoo Park, Yoon Ho Ko |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | 25% |
| Unknown | 3 | 75% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Practitioners (doctors, other healthcare professionals) | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 17 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Unspecified | 3 | 18% |
| Student > Ph. D. Student | 2 | 12% |
| Researcher | 2 | 12% |
| Student > Bachelor | 1 | 6% |
| Lecturer > Senior Lecturer | 1 | 6% |
| Other | 2 | 12% |
| Unknown | 6 | 35% |
| Readers by discipline | Count | As % |
|---|---|---|
| Unspecified | 3 | 18% |
| Medicine and Dentistry | 2 | 12% |
| Computer Science | 2 | 12% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 6% |
| Engineering | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 7 | 41% |
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 24 July 2021.
All research outputs
#17,297,846
of 25,392,582 outputs
Outputs from Frontiers in oncology
#8,039
of 22,436 outputs
Outputs of similar age
#274,547
of 450,754 outputs
Outputs of similar age from Frontiers in oncology
#468
of 1,410 outputs
Altmetric has tracked 25,392,582 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 22,436 research outputs from this source. They receive a mean Attention Score of 3.0. This one has gotten more attention than average, scoring higher than 58% 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 450,754 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,410 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.