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 |
Diagnostic accuracy of code-free deep learning for detection and evaluation of posterior capsule opacification
|
|---|---|
| Published in |
BMJ Open Ophthalmology, May 2022
|
| DOI | 10.1136/bmjophth-2022-000992 |
| Pubmed ID | |
| Authors |
Josef Huemer, Martin Kronschläger, Manuel Ruiss, Dawn Sim, Pearse A Keane, Oliver Findl, Siegfried K Wagner |
X Demographics
The data shown below were collected from the profiles of 16 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 | 6 | 38% |
| United States | 1 | 6% |
| Unknown | 9 | 56% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 11 | 69% |
| Practitioners (doctors, other healthcare professionals) | 4 | 25% |
| Science communicators (journalists, bloggers, editors) | 1 | 6% |
Mendeley readers
The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 16 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 2 | 13% |
| Student > Postgraduate | 2 | 13% |
| Other | 1 | 6% |
| Student > Ph. D. Student | 1 | 6% |
| Student > Doctoral Student | 1 | 6% |
| Other | 2 | 13% |
| Unknown | 7 | 44% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 3 | 19% |
| Engineering | 2 | 13% |
| Economics, Econometrics and Finance | 1 | 6% |
| Computer Science | 1 | 6% |
| Unknown | 9 | 56% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 20 December 2022.
All research outputs
#3,058,300
of 23,914,147 outputs
Outputs from BMJ Open Ophthalmology
#25
of 331 outputs
Outputs of similar age
#66,659
of 428,112 outputs
Outputs of similar age from BMJ Open Ophthalmology
#1
of 15 outputs
Altmetric has tracked 23,914,147 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 331 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.5. This one has done particularly well, scoring higher than 92% 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 428,112 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 15 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 99% of its contemporaries.