The data shown below were collected from the profiles of 44 X users who shared this research output. Click here to find out more about how the information was compiled.
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| Title |
Development of a treatment selection algorithm for SGLT2 and DPP-4 inhibitor therapies in people with type 2 diabetes: a retrospective cohort study
|
|---|---|
| Published in |
The Lancet Digital Health, December 2022
|
| DOI | 10.1016/s2589-7500(22)00174-1 |
| Pubmed ID | |
| Authors |
John M Dennis, Katherine G Young, Andrew P McGovern, Bilal A Mateen, Sebastian J Vollmer, Michael D Simpson, William E Henley, Rury R Holman, Naveed Sattar, Ewan R Pearson, Andrew T Hattersley, Angus G Jones, Beverley M Shields, MASTERMIND consortium |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 8 | 18% |
| Spain | 2 | 5% |
| Peru | 1 | 2% |
| Belgium | 1 | 2% |
| Unknown | 32 | 73% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 29 | 66% |
| Scientists | 9 | 20% |
| Practitioners (doctors, other healthcare professionals) | 5 | 11% |
| Science communicators (journalists, bloggers, editors) | 1 | 2% |
Mendeley readers
The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 31 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 5 | 16% |
| Student > Ph. D. Student | 3 | 10% |
| Student > Bachelor | 3 | 10% |
| Researcher | 2 | 6% |
| Student > Doctoral Student | 2 | 6% |
| Other | 1 | 3% |
| Unknown | 15 | 48% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 6 | 19% |
| Biochemistry, Genetics and Molecular Biology | 4 | 13% |
| Mathematics | 1 | 3% |
| Nursing and Health Professions | 1 | 3% |
| Economics, Econometrics and Finance | 1 | 3% |
| Other | 3 | 10% |
| Unknown | 15 | 48% |
Attention Score in Context
This research output has an Altmetric Attention Score of 34. 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 13 October 2023.
All research outputs
#1,220,286
of 25,992,468 outputs
Outputs from The Lancet Digital Health
#1
of 1 outputs
Outputs of similar age
#26,805
of 494,142 outputs
Outputs of similar age from The Lancet Digital Health
#1
of 1 outputs
Altmetric has tracked 25,992,468 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 33.4. This one scored the same or higher as 0 of them.
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 494,142 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 94% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them