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Framework for the impact analysis and implementation of Clinical Prediction Rules (CPRs)

Overview of attention for article published in BMC Medical Informatics and Decision Making, October 2011
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Title
Framework for the impact analysis and implementation of Clinical Prediction Rules (CPRs)
Published in
BMC Medical Informatics and Decision Making, October 2011
DOI 10.1186/1472-6947-11-62
Pubmed ID
Authors

Emma Wallace, Susan M Smith, Rafael Perera-Salazar, Paul Vaucher, Colin McCowan, Gary Collins, Jan Verbakel, Monica Lakhanpaul, Tom Fahey, (Members of the International Diagnostic and Prognosis Prediction (IDAPP) group)

Abstract

Clinical Prediction Rules (CPRs) are tools that quantify the contribution of symptoms, clinical signs and available diagnostic tests, and in doing so stratify patients according to the probability of having a target outcome or need for a specified treatment. Most focus on the derivation stage with only a minority progressing to validation and very few undergoing impact analysis. Impact analysis studies remain the most efficient way of assessing whether incorporating CPRs into a decision making process improves patient care. However there is a lack of clear methodology for the design of high quality impact analysis studies.We have developed a sequential four-phased framework based on the literature and the collective experience of our international working group to help researchers identify and overcome the specific challenges in designing and conducting an impact analysis of a CPR.There is a need to shift emphasis from deriving new CPRs to validating and implementing existing CPRs. The proposed framework provides a structured approach to this topical and complex area of research.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 131 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 2%
Canada 2 2%
Australia 1 <1%
Unknown 126 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 23%
Researcher 18 14%
Student > Master 14 11%
Professor 10 8%
Student > Doctoral Student 9 7%
Other 27 21%
Unknown 23 18%
Readers by discipline Count As %
Medicine and Dentistry 57 44%
Computer Science 14 11%
Nursing and Health Professions 8 6%
Agricultural and Biological Sciences 7 5%
Biochemistry, Genetics and Molecular Biology 3 2%
Other 14 11%
Unknown 28 21%
Attention Score in Context

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 15 October 2011.
All research outputs
#18,297,449
of 22,653,392 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,561
of 1,978 outputs
Outputs of similar age
#113,285
of 136,361 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#15
of 16 outputs
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We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one is in the 6th percentile – i.e., 6% of its contemporaries scored the same or lower than it.