| Title |
Can diabetic polyneuropathy and foot ulcers in patients with type 2 diabetes be accurately identified based on ICD-10 hospital diagnoses and drug prescriptions?
|
|---|---|
| Published in |
Clinical Epidemiology, May 2019
|
| DOI | 10.2147/clep.s197474 |
| Pubmed ID | |
| Authors |
Diana Hedevang Christensen, Søren Tang Knudsen, Sia Kromann Nicolaisen, Henning Andersen, Brian Christopher Callaghan, Nanna Brix Finnerup, Troels Staehelin Jensen, Reimar Wernich Thomsen |
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 % |
|---|---|---|
| Mexico | 1 | 33% |
| United States | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Practitioners (doctors, other healthcare professionals) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 48 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 8 | 17% |
| Student > Ph. D. Student | 5 | 10% |
| Professor > Associate Professor | 4 | 8% |
| Unspecified | 3 | 6% |
| Student > Bachelor | 2 | 4% |
| Other | 9 | 19% |
| Unknown | 17 | 35% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 10 | 21% |
| Unspecified | 3 | 6% |
| Nursing and Health Professions | 3 | 6% |
| Engineering | 2 | 4% |
| Chemistry | 2 | 4% |
| Other | 7 | 15% |
| Unknown | 21 | 44% |
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 16 May 2019.
All research outputs
#13,293,270
of 23,144,579 outputs
Outputs from Clinical Epidemiology
#357
of 728 outputs
Outputs of similar age
#168,062
of 350,403 outputs
Outputs of similar age from Clinical Epidemiology
#12
of 18 outputs
Altmetric has tracked 23,144,579 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 728 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.4. This one is in the 49th percentile – i.e., 49% 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 350,403 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 51% of its contemporaries.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.