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A regret theory approach to decision curve analysis: A novel method for eliciting decision makers' preferences and decision-making

Overview of attention for article published in BMC Medical Informatics and Decision Making, September 2010
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  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
47 Dimensions

Readers on

mendeley
94 Mendeley
Title
A regret theory approach to decision curve analysis: A novel method for eliciting decision makers' preferences and decision-making
Published in
BMC Medical Informatics and Decision Making, September 2010
DOI 10.1186/1472-6947-10-51
Pubmed ID
Authors

Athanasios Tsalatsanis, Iztok Hozo, Andrew Vickers, Benjamin Djulbegovic

Abstract

Decision curve analysis (DCA) has been proposed as an alternative method for evaluation of diagnostic tests, prediction models, and molecular markers. However, DCA is based on expected utility theory, which has been routinely violated by decision makers. Decision-making is governed by intuition (system 1), and analytical, deliberative process (system 2), thus, rational decision-making should reflect both formal principles of rationality and intuition about good decisions. We use the cognitive emotion of regret to serve as a link between systems 1 and 2 and to reformulate DCA.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters 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 94 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
Ecuador 1 1%
Vietnam 1 1%
Italy 1 1%
Brazil 1 1%
India 1 1%
United Kingdom 1 1%
Hungary 1 1%
Unknown 84 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 22 23%
Student > Ph. D. Student 16 17%
Professor 9 10%
Student > Master 9 10%
Student > Doctoral Student 8 9%
Other 27 29%
Unknown 3 3%
Readers by discipline Count As %
Medicine and Dentistry 39 41%
Engineering 10 11%
Psychology 8 9%
Computer Science 7 7%
Business, Management and Accounting 6 6%
Other 18 19%
Unknown 6 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 28 July 2015.
All research outputs
#8,493,367
of 14,668,766 outputs
Outputs from BMC Medical Informatics and Decision Making
#781
of 1,343 outputs
Outputs of similar age
#74,420
of 149,726 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#99
of 155 outputs
Altmetric has tracked 14,668,766 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,343 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one is in the 35th percentile – i.e., 35% 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 149,726 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 155 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.