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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A simple statistical model for prediction of acute coronary syndrome in chest pain patients in the emergency department
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, July 2006
|
| DOI | 10.1186/1472-6947-6-28 |
| Pubmed ID | |
| Authors |
Jonas Björk, Jakob L Forberg, Mattias Ohlsson, Lars Edenbrandt, Hans Öhlin, Ulf Ekelund |
| Abstract |
Several models for prediction of acute coronary syndrome (ACS) among chest pain patients in the emergency department (ED) have been presented, but many models predict only the likelihood of acute myocardial infarction, or include a large number of variables, which make them less than optimal for implementation at a busy ED. We report here a simple statistical model for ACS prediction that could be used in routine care at a busy ED. |
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 % |
|---|---|---|
| India | 2 | 40% |
| Unknown | 3 | 60% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 3 | 60% |
| Science communicators (journalists, bloggers, editors) | 1 | 20% |
| Members of the public | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 2% |
| Sweden | 1 | 2% |
| Germany | 1 | 2% |
| Unknown | 62 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 13 | 20% |
| Student > Doctoral Student | 8 | 12% |
| Student > Ph. D. Student | 7 | 11% |
| Student > Bachelor | 7 | 11% |
| Student > Master | 7 | 11% |
| Other | 20 | 31% |
| Unknown | 3 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 37 | 57% |
| Computer Science | 6 | 9% |
| Engineering | 6 | 9% |
| Nursing and Health Professions | 5 | 8% |
| Social Sciences | 3 | 5% |
| Other | 4 | 6% |
| Unknown | 4 | 6% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 23 August 2021.
All research outputs
#13,155,047
of 22,715,151 outputs
Outputs from BMC Medical Informatics and Decision Making
#943
of 1,982 outputs
Outputs of similar age
#55,350
of 65,353 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#5
of 8 outputs
Altmetric has tracked 22,715,151 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,982 research outputs from this source. They receive a mean Attention Score of 4.9. 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 65,353 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.