| Title |
Using machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: a systematic review
|
|---|---|
| Published in |
Diagnostic and Prognostic Research, October 2020
|
| DOI | 10.1186/s41512-020-00084-1 |
| Pubmed ID | |
| Authors |
Jamie Miles, Janette Turner, Richard Jacques, Julia Williams, Suzanne Mason |
X Demographics
The data shown below were collected from the profiles of 26 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 Kingdom | 12 | 46% |
| United States | 3 | 12% |
| Netherlands | 1 | 4% |
| South Africa | 1 | 4% |
| Saudi Arabia | 1 | 4% |
| France | 1 | 4% |
| Unknown | 7 | 27% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 12 | 46% |
| Members of the public | 10 | 38% |
| Practitioners (doctors, other healthcare professionals) | 4 | 15% |
Mendeley readers
The data shown below were compiled from readership statistics for 94 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 94 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 11 | 12% |
| Student > Ph. D. Student | 10 | 11% |
| Unspecified | 9 | 10% |
| Student > Bachelor | 6 | 6% |
| Researcher | 5 | 5% |
| Other | 16 | 17% |
| Unknown | 37 | 39% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 12 | 13% |
| Unspecified | 9 | 10% |
| Computer Science | 8 | 9% |
| Engineering | 8 | 9% |
| Nursing and Health Professions | 4 | 4% |
| Other | 13 | 14% |
| Unknown | 40 | 43% |
Attention Score in Context
This research output has an Altmetric Attention Score of 30. 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 11 July 2023.
All research outputs
#1,217,703
of 24,053,881 outputs
Outputs from Diagnostic and Prognostic Research
#11
of 116 outputs
Outputs of similar age
#34,075
of 415,706 outputs
Outputs of similar age from Diagnostic and Prognostic Research
#1
of 4 outputs
Altmetric has tracked 24,053,881 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 116 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.8. This one has done particularly well, scoring higher than 91% 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 415,706 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 91% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them