| Title |
A clinical prediction model to identify patients at high risk of death in the emergency department
|
|---|---|
| Published in |
Intensive Care Medicine, March 2015
|
| DOI | 10.1007/s00134-015-3737-x |
| Pubmed ID | |
| Authors |
Michael Coslovsky, Jukka Takala, Aristomenis K. Exadaktylos, Luca Martinolli, Tobias M. Merz |
X Demographics
The data shown below were collected from the profiles of 5 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 | 1 | 20% |
| Ecuador | 1 | 20% |
| Unknown | 3 | 60% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 2 | 40% |
| Members of the public | 2 | 40% |
| Scientists | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 134 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | <1% |
| United States | 1 | <1% |
| Unknown | 132 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 18 | 13% |
| Student > Master | 18 | 13% |
| Student > Ph. D. Student | 17 | 13% |
| Other | 14 | 10% |
| Student > Doctoral Student | 10 | 7% |
| Other | 29 | 22% |
| Unknown | 28 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 72 | 54% |
| Nursing and Health Professions | 12 | 9% |
| Engineering | 5 | 4% |
| Computer Science | 5 | 4% |
| Agricultural and Biological Sciences | 4 | 3% |
| Other | 5 | 4% |
| Unknown | 31 | 23% |
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 31 October 2017.
All research outputs
#5,538,221
of 22,803,211 outputs
Outputs from Intensive Care Medicine
#2,408
of 4,981 outputs
Outputs of similar age
#64,062
of 262,953 outputs
Outputs of similar age from Intensive Care Medicine
#27
of 90 outputs
Altmetric has tracked 22,803,211 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 4,981 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.0. This one has gotten more attention than average, scoring higher than 51% 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 262,953 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 90 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 70% of its contemporaries.