| Title |
Epiretinal membrane: optical coherence tomography-based diagnosis and classification
|
|---|---|
| Published in |
Clinical Ophthalmology, March 2016
|
| DOI | 10.2147/opth.s97722 |
| Pubmed ID | |
| Authors |
William Stevenson, Claudia M Prospero Ponce, Daniel R Agarwal, Rachel Gelman, John B Christoforidis |
| Abstract |
Epiretinal membrane (ERM) is a disorder of the vitreomacular interface characterized by symptoms of decreased visual acuity and metamorphopsia. The diagnosis and classification of ERM has traditionally been based on clinical examination findings. However, modern optical coherence tomography (OCT) has proven to be more sensitive than clinical examination for the diagnosis of ERM. Furthermore, OCT-derived findings, such as central foveal thickness and inner segment ellipsoid band integrity, have shown clinical relevance in the setting of ERM. To date, no OCT-based ERM classification scheme has been widely accepted for use in clinical practice and investigation. Herein, we review the pathogenesis, diagnosis, and classification of ERMs and propose an OCT-based ERM classification system. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 151 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Poland | 1 | <1% |
| Unknown | 150 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 27 | 18% |
| Student > Master | 15 | 10% |
| Researcher | 13 | 9% |
| Student > Doctoral Student | 13 | 9% |
| Student > Ph. D. Student | 13 | 9% |
| Other | 32 | 21% |
| Unknown | 38 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 78 | 52% |
| Nursing and Health Professions | 9 | 6% |
| Unspecified | 7 | 5% |
| Agricultural and Biological Sciences | 3 | 2% |
| Computer Science | 2 | 1% |
| Other | 7 | 5% |
| Unknown | 45 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 18 October 2017.
All research outputs
#3,621,892
of 25,374,647 outputs
Outputs from Clinical Ophthalmology
#303
of 3,712 outputs
Outputs of similar age
#54,899
of 312,602 outputs
Outputs of similar age from Clinical Ophthalmology
#7
of 62 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,712 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done particularly well, scoring higher than 91% of its peers.
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