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 |
Developing an algorithm to identify people with Chronic Obstructive Pulmonary Disease (COPD) using administrative data
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, May 2012
|
| DOI | 10.1186/1472-6947-12-38 |
| Pubmed ID | |
| Authors |
Margrethe Smidth, Ineta Sokolowski, Lone Kærsvang, Peter Vedsted |
| Abstract |
An important prerequisite for the Chronic Care Model is to be able to identify, in a valid, simple and inexpensive way, the population with a chronic condition that needs proactive and planned care. We investigated if a set of administrative data could be used to identify patients with Chronic Obstructive Pulmonary Disease in a Danish population. |
X Demographics
The data shown below were collected from the profiles of 3 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 | 1 | 33% |
| Mexico | 1 | 33% |
| United Kingdom | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 33% |
| Members of the public | 1 | 33% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Croatia | 1 | 2% |
| Portugal | 1 | 2% |
| Unknown | 48 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 22% |
| Student > Ph. D. Student | 9 | 18% |
| Student > Master | 8 | 16% |
| Student > Bachelor | 3 | 6% |
| Student > Doctoral Student | 2 | 4% |
| Other | 6 | 12% |
| Unknown | 11 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 19 | 38% |
| Nursing and Health Professions | 3 | 6% |
| Computer Science | 3 | 6% |
| Social Sciences | 3 | 6% |
| Agricultural and Biological Sciences | 1 | 2% |
| Other | 4 | 8% |
| Unknown | 17 | 34% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 24 May 2012.
All research outputs
#14,144,226
of 22,665,794 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,101
of 1,978 outputs
Outputs of similar age
#96,257
of 164,244 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#21
of 38 outputs
Altmetric has tracked 22,665,794 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,978 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 38th percentile – i.e., 38% of its peers scored the same or lower than it.
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 164,244 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.