The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Deep learning based CT images automatic analysis model for active/non-active pulmonary tuberculosis differential diagnosis
|
|---|---|
| Published in |
Frontiers in Molecular Biosciences, December 2022
|
| DOI | 10.3389/fmolb.2022.1086047 |
| Pubmed ID | |
| Authors |
Mayidili Nijiati, Renbing Zhou, Miriguli Damaola, Chuling Hu, Li Li, Baoxin Qian, Abudukeyoumujiang Abulizi, Aihemaitijiang Kaisaier, Chao Cai, Hongjun Li, Xiaoguang Zou |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 16 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 3 | 19% |
| Researcher | 3 | 19% |
| Student > Doctoral Student | 2 | 13% |
| Lecturer | 2 | 13% |
| Student > Master | 1 | 6% |
| Other | 0 | 0% |
| Unknown | 5 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 2 | 13% |
| Medicine and Dentistry | 2 | 13% |
| Nursing and Health Professions | 2 | 13% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 6% |
| Biochemistry, Genetics and Molecular Biology | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 7 | 44% |
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 28 December 2022.
All research outputs
#18,913,395
of 23,437,201 outputs
Outputs from Frontiers in Molecular Biosciences
#2,073
of 4,056 outputs
Outputs of similar age
#305,172
of 443,271 outputs
Outputs of similar age from Frontiers in Molecular Biosciences
#156
of 293 outputs
Altmetric has tracked 23,437,201 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,056 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 33rd percentile – i.e., 33% 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 443,271 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 293 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.