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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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  • Good Attention Score compared to outputs of the same age (65th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

Mentioned by

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7 X users

Citations

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74 Dimensions

Readers on

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107 Mendeley
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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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 107 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
Italy 1 <1%
Vietnam 1 <1%
Hungary 1 <1%
Brazil 1 <1%
Ecuador 1 <1%
United Kingdom 1 <1%
India 1 <1%
Unknown 97 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 22 21%
Student > Ph. D. Student 19 18%
Student > Master 12 11%
Student > Doctoral Student 9 8%
Professor 9 8%
Other 28 26%
Unknown 8 7%
Readers by discipline Count As %
Medicine and Dentistry 39 36%
Engineering 10 9%
Psychology 10 9%
Computer Science 8 7%
Business, Management and Accounting 7 7%
Other 20 19%
Unknown 13 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 12 June 2023.
All research outputs
#7,149,194
of 23,852,694 outputs
Outputs from BMC Medical Informatics and Decision Making
#671
of 2,046 outputs
Outputs of similar age
#29,757
of 87,689 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#8
of 15 outputs
Altmetric has tracked 23,852,694 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 2,046 research outputs from this source. They receive a mean Attention Score of 5.0. This one has gotten more attention than average, scoring higher than 66% 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 87,689 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.