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Mendeley readers
Attention Score in Context
Title |
Using Stochastic modelling to identify unusual continuous glucose monitor measurements and behaviour, in newborn infants
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Published in |
BioMedical Engineering OnLine, August 2012
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DOI | 10.1186/1475-925x-11-45 |
Pubmed ID | |
Authors |
Matthew Signal, Aaron Le Compte, Deborah L Harris, Phil J Weston, Jane E Harding, J Geoffrey Chase |
Abstract |
Abnormal blood glucose (BG) concentrations have been associated with increased morbidity and mortality in both critically ill adults and infants. Furthermore, hypoglycaemia and glycaemic variability have both been independently linked to mortality in these patients. Continuous Glucose Monitoring (CGM) devices have the potential to improve detection and diagnosis of these glycaemic abnormalities. However, sensor noise is a trade-off of the high measurement rate and must be managed effectively if CGMs are going to be used to monitor, diagnose and potentially help treat glycaemic abnormalities. |
X Demographics
The data shown below were collected from the profiles of 2 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 | 1 | 50% |
Mexico | 1 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
New Zealand | 1 | 3% |
Canada | 1 | 3% |
Unknown | 33 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 7 | 20% |
Student > Ph. D. Student | 5 | 14% |
Student > Master | 4 | 11% |
Student > Postgraduate | 3 | 9% |
Student > Doctoral Student | 3 | 9% |
Other | 8 | 23% |
Unknown | 5 | 14% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 9 | 26% |
Engineering | 8 | 23% |
Nursing and Health Professions | 3 | 9% |
Social Sciences | 2 | 6% |
Computer Science | 1 | 3% |
Other | 4 | 11% |
Unknown | 8 | 23% |
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 07 August 2012.
All research outputs
#19,944,994
of 25,374,647 outputs
Outputs from BioMedical Engineering OnLine
#578
of 867 outputs
Outputs of similar age
#138,802
of 182,990 outputs
Outputs of similar age from BioMedical Engineering OnLine
#12
of 28 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 867 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one is in the 30th percentile – i.e., 30% 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 182,990 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.