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Mendeley readers
Attention Score in Context
Title |
Using ROC Curves to Choose Minimally Important Change Thresholds when Sensitivity and Specificity Are Valued Equally: The Forgotten Lesson of Pythagoras. Theoretical Considerations and an Example Application of Change in Health Status
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Published in |
PLOS ONE, December 2014
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DOI | 10.1371/journal.pone.0114468 |
Pubmed ID | |
Authors |
Robert Froud, Gary Abel |
Abstract |
Receiver Operator Characteristic (ROC) curves are being used to identify Minimally Important Change (MIC) thresholds on scales that measure a change in health status. In quasi-continuous patient reported outcome measures, such as those that measure changes in chronic diseases with variable clinical trajectories, sensitivity and specificity are often valued equally. Notwithstanding methodologists agreeing that these should be valued equally, different approaches have been taken to estimating MIC thresholds using ROC curves. |
X Demographics
The data shown below were collected from the profiles of 17 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 States | 3 | 18% |
United Kingdom | 2 | 12% |
Egypt | 1 | 6% |
Indonesia | 1 | 6% |
Spain | 1 | 6% |
Unknown | 9 | 53% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 12 | 71% |
Scientists | 4 | 24% |
Science communicators (journalists, bloggers, editors) | 1 | 6% |
Mendeley readers
The data shown below were compiled from readership statistics for 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Brazil | 2 | 3% |
Israel | 1 | 1% |
Denmark | 1 | 1% |
United States | 1 | 1% |
Unknown | 71 | 93% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 16 | 21% |
Student > Ph. D. Student | 8 | 11% |
Student > Bachelor | 8 | 11% |
Professor > Associate Professor | 6 | 8% |
Professor | 5 | 7% |
Other | 22 | 29% |
Unknown | 11 | 14% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 29 | 38% |
Unspecified | 5 | 7% |
Agricultural and Biological Sciences | 4 | 5% |
Nursing and Health Professions | 4 | 5% |
Chemistry | 2 | 3% |
Other | 12 | 16% |
Unknown | 20 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 109. 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 15 April 2018.
All research outputs
#392,919
of 25,837,817 outputs
Outputs from PLOS ONE
#5,538
of 224,660 outputs
Outputs of similar age
#4,434
of 372,635 outputs
Outputs of similar age from PLOS ONE
#99
of 4,049 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 224,660 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.8. This one has done particularly well, scoring higher than 97% 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 372,635 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 98% of its contemporaries.
We're also able to compare this research output to 4,049 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 97% of its contemporaries.