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Correlation between short-term and long-term intraocular pressure fluctuation in glaucoma patients

Overview of attention for article published in Clinical Ophthalmology, September 2016
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Citations

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Title
Correlation between short-term and long-term intraocular pressure fluctuation in glaucoma patients
Published in
Clinical Ophthalmology, September 2016
DOI 10.2147/opth.s116859
Pubmed ID
Authors

Atsushi Hayashi, Naoki Tojo, Shinya Abe, Mari Miyakoshi

Abstract

We investigated correlations between short-term and long-term intraocular pressure (IOP) fluctuations. We examined 50 eyes of glaucoma patients who were followed for >2 years. We measured short-term IOP fluctuation using a Triggerfish(®) contact lens sensor (CLS). The short-term IOP fluctuation (mVeq) was defined as the difference between the maximum value and the minimum value measured during the 24-hour course with CLS. The long-term IOP fluctuation was defined by four parameters: 1) the mean IOP (mmHg) determined during follow-up; 2) the IOP difference, which was defined as the difference between the maximum IOP and the minimum IOP; 3) the standard deviation of IOP; and 4) the peak IOP, which was defined as the maximum IOP. Correlations between these parameters and the short-term IOP fluctuation were examined. The mean follow-up period was 5.4 years. The average IOP was 15.0±4.0 mmHg. The range of short-term IOP fluctuation identified with CLS was significantly correlated with all the four long-term IOP fluctuation parameters. Short-term IOP fluctuations were found to be associated with long-term IOP fluctuations. Examination of 24-hour IOP fluctuations with the CLS might be useful for predicting the long-term IOP fluctuation.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 25%
Student > Doctoral Student 2 17%
Student > Postgraduate 2 17%
Professor 1 8%
Researcher 1 8%
Other 1 8%
Unknown 2 17%
Readers by discipline Count As %
Medicine and Dentistry 8 67%
Neuroscience 1 8%
Social Sciences 1 8%
Engineering 1 8%
Unknown 1 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 05 September 2016.
All research outputs
#9,995,755
of 12,488,808 outputs
Outputs from Clinical Ophthalmology
#1,036
of 1,597 outputs
Outputs of similar age
#185,476
of 262,134 outputs
Outputs of similar age from Clinical Ophthalmology
#46
of 84 outputs
Altmetric has tracked 12,488,808 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,597 research outputs from this source. They receive a mean Attention Score of 3.2. This one is in the 10th percentile – i.e., 10% 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 262,134 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 84 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.