You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography
|
|---|---|
| Published in |
Journal of Clinical Oncology, January 2023
|
| DOI | 10.1200/jco.22.01345 |
| Pubmed ID | |
| Authors |
Peter G. Mikhael, Jeremy Wohlwend, Adam Yala, Ludvig Karstens, Justin Xiang, Angelo K. Takigami, Patrick P. Bourgouin, PuiYee Chan, Sofiane Mrah, Wael Amayri, Yu-Hsiang Juan, Cheng-Ta Yang, Yung-Liang Wan, Gigin Lin, Lecia V. Sequist, Florian J. Fintelmann, Regina Barzilay |
X Demographics
The data shown below were collected from the profiles of 136 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 39 | 29% |
| France | 7 | 5% |
| United Kingdom | 5 | 4% |
| India | 5 | 4% |
| Spain | 3 | 2% |
| Colombia | 3 | 2% |
| Germany | 2 | 1% |
| Turkey | 2 | 1% |
| Italy | 2 | 1% |
| Other | 15 | 11% |
| Unknown | 53 | 39% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 86 | 63% |
| Scientists | 23 | 17% |
| Practitioners (doctors, other healthcare professionals) | 22 | 16% |
| Science communicators (journalists, bloggers, editors) | 5 | 4% |
Mendeley readers
The data shown below were compiled from readership statistics for 117 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 117 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 21 | 18% |
| Student > Ph. D. Student | 11 | 9% |
| Student > Postgraduate | 6 | 5% |
| Other | 6 | 5% |
| Student > Bachelor | 5 | 4% |
| Other | 12 | 10% |
| Unknown | 56 | 48% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 20 | 17% |
| Engineering | 11 | 9% |
| Computer Science | 9 | 8% |
| Agricultural and Biological Sciences | 6 | 5% |
| Unspecified | 3 | 3% |
| Other | 10 | 9% |
| Unknown | 58 | 50% |
Attention Score in Context
This research output has an Altmetric Attention Score of 986. 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 25 April 2024.
All research outputs
#16,857
of 25,808,886 outputs
Outputs from Journal of Clinical Oncology
#12
of 22,261 outputs
Outputs of similar age
#493
of 480,614 outputs
Outputs of similar age from Journal of Clinical Oncology
#2
of 354 outputs
Altmetric has tracked 25,808,886 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 22,261 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 21.2. This one has done particularly well, scoring higher than 99% 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 480,614 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 354 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.