| Title |
Development of machine learning models to predict RT-PCR results for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in patients with influenza-like symptoms using only basic clinical data
|
|---|---|
| Published in |
Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, December 2020
|
| DOI | 10.1186/s13049-020-00808-8 |
| Pubmed ID | |
| Authors |
Thomas Langer, Martina Favarato, Riccardo Giudici, Gabriele Bassi, Roberta Garberi, Fabiana Villa, Hedwige Gay, Anna Zeduri, Sara Bragagnolo, Alberto Molteni, Andrea Beretta, Matteo Corradin, Mauro Moreno, Chiara Vismara, Carlo Federico Perno, Massimo Buscema, Enzo Grossi, Roberto Fumagalli |
X Demographics
The data shown below were collected from the profiles of 11 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 | 3 | 27% |
| Italy | 1 | 9% |
| Unknown | 7 | 64% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 9 | 82% |
| Scientists | 1 | 9% |
| Science communicators (journalists, bloggers, editors) | 1 | 9% |
Mendeley readers
The data shown below were compiled from readership statistics for 110 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 110 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 13 | 12% |
| Student > Master | 11 | 10% |
| Student > Bachelor | 10 | 9% |
| Professor | 6 | 5% |
| Student > Doctoral Student | 5 | 5% |
| Other | 23 | 21% |
| Unknown | 42 | 38% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 26 | 24% |
| Nursing and Health Professions | 9 | 8% |
| Agricultural and Biological Sciences | 5 | 5% |
| Computer Science | 4 | 4% |
| Social Sciences | 4 | 4% |
| Other | 16 | 15% |
| Unknown | 46 | 42% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 14 December 2020.
All research outputs
#5,952,978
of 23,885,338 outputs
Outputs from Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine
#501
of 1,287 outputs
Outputs of similar age
#140,213
of 514,051 outputs
Outputs of similar age from Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine
#18
of 33 outputs
Altmetric has tracked 23,885,338 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,287 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.4. This one has gotten more attention than average, scoring higher than 60% 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 514,051 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 72% of its contemporaries.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.