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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Identifying patients with diabetes and the earliest date of diagnosis in real time: an electronic health record case-finding algorithm
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, August 2013
|
| DOI | 10.1186/1472-6947-13-81 |
| Pubmed ID | |
| Authors |
Anil N Makam, Oanh K Nguyen, Billy Moore, Ying Ma, Ruben Amarasingham |
| Abstract |
Effective population management of patients with diabetes requires timely recognition. Current case-finding algorithms can accurately detect patients with diabetes, but lack real-time identification. We sought to develop and validate an automated, real-time diabetes case-finding algorithm to identify patients with diabetes at the earliest possible date. |
X Demographics
The data shown below were collected from the profiles of 12 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 | 5 | 42% |
| Switzerland | 1 | 8% |
| India | 1 | 8% |
| United Kingdom | 1 | 8% |
| Panama | 1 | 8% |
| Unknown | 3 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 42% |
| Practitioners (doctors, other healthcare professionals) | 3 | 25% |
| Scientists | 2 | 17% |
| Science communicators (journalists, bloggers, editors) | 2 | 17% |
Mendeley readers
The data shown below were compiled from readership statistics for 114 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% |
| Ireland | 1 | <1% |
| Ghana | 1 | <1% |
| Australia | 1 | <1% |
| Switzerland | 1 | <1% |
| Austria | 1 | <1% |
| United States | 1 | <1% |
| Unknown | 103 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 28 | 25% |
| Researcher | 20 | 18% |
| Student > Master | 19 | 17% |
| Other | 7 | 6% |
| Student > Postgraduate | 6 | 5% |
| Other | 23 | 20% |
| Unknown | 11 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 28 | 25% |
| Computer Science | 14 | 12% |
| Business, Management and Accounting | 10 | 9% |
| Engineering | 7 | 6% |
| Social Sciences | 6 | 5% |
| Other | 28 | 25% |
| Unknown | 21 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 20. 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 06 November 2018.
All research outputs
#1,585,558
of 22,715,151 outputs
Outputs from BMC Medical Informatics and Decision Making
#73
of 1,982 outputs
Outputs of similar age
#14,647
of 198,390 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 40 outputs
Altmetric has tracked 22,715,151 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,982 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done particularly well, scoring higher than 96% 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 198,390 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.