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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Optimising Use of Electronic Health Records to Describe the Presentation of Rheumatoid Arthritis in Primary Care: A Strategy for Developing Code Lists
|
|---|---|
| Published in |
PLOS ONE, February 2013
|
| DOI | 10.1371/journal.pone.0054878 |
| Pubmed ID | |
| Authors |
Amanda Nicholson, Elizabeth Ford, Kevin A. Davies, Helen E. Smith, Greta Rait, A. Rosemary Tate, Irene Petersen, Jackie Cassell |
| Abstract |
Research using electronic health records (EHRs) relies heavily on coded clinical data. Due to variation in coding practices, it can be difficult to aggregate the codes for a condition in order to define cases. This paper describes a methodology to develop 'indicator markers' found in patients with early rheumatoid arthritis (RA); these are a broader range of codes which may allow a probabilistic case definition to use in cases where no diagnostic code is yet recorded. |
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 Kingdom | 3 | 60% |
| United States | 1 | 20% |
| Unknown | 1 | 20% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 60% |
| Practitioners (doctors, other healthcare professionals) | 1 | 20% |
| Scientists | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 3 | 3% |
| United Kingdom | 2 | 2% |
| Netherlands | 1 | 1% |
| Switzerland | 1 | 1% |
| Indonesia | 1 | 1% |
| Unknown | 78 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 23 | 27% |
| Researcher | 21 | 24% |
| Student > Master | 6 | 7% |
| Student > Postgraduate | 5 | 6% |
| Other | 5 | 6% |
| Other | 12 | 14% |
| Unknown | 14 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 36 | 42% |
| Computer Science | 5 | 6% |
| Engineering | 4 | 5% |
| Economics, Econometrics and Finance | 3 | 3% |
| Social Sciences | 3 | 3% |
| Other | 15 | 17% |
| Unknown | 20 | 23% |
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 29 April 2015.
All research outputs
#12,580,081
of 22,699,621 outputs
Outputs from PLOS ONE
#97,180
of 193,796 outputs
Outputs of similar age
#97,079
of 192,954 outputs
Outputs of similar age from PLOS ONE
#2,450
of 5,396 outputs
Altmetric has tracked 22,699,621 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 193,796 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.0. 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 192,954 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5,396 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 54% of its contemporaries.