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Conceptualization of category-oriented likelihood ratio: a useful tool for clinical diagnostic reasoning

Overview of attention for article published in BMC Medical Education, November 2011
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Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
8 Dimensions

Readers on

mendeley
57 Mendeley
citeulike
2 CiteULike
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Title
Conceptualization of category-oriented likelihood ratio: a useful tool for clinical diagnostic reasoning
Published in
BMC Medical Education, November 2011
DOI 10.1186/1472-6920-11-94
Pubmed ID
Authors

Hamideh Moosapour, Mohsin Raza, Mehdi Rambod, Akbar Soltani

Abstract

In the diagnostic reasoning process medical students and novice physicians need to be made aware of the diagnostic values of the clinical findings (including history, signs, and symptoms) to make an appropriate diagnostic decision. Diagnostic reasoning has been understood in light of two paradigms on clinical reasoning: problem solving and decision making. They advocate the reasoning strategies used by expert physicians and the statistical models of reasoning, respectively. Evidence-based medicine (EBM) applies decision theory to the clinical diagnosis, which can be a challenging topic in medical education.This theoretical article tries to compare evidence-based diagnosis with expert-based strategies in clinical diagnosis and also defines a novel concept of category-oriented likelihood ratio (LR) to propose a new model combining both aforementioned methods.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Unknown 56 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 21%
Student > Master 7 12%
Student > Bachelor 7 12%
Student > Ph. D. Student 5 9%
Student > Doctoral Student 4 7%
Other 17 30%
Unknown 5 9%
Readers by discipline Count As %
Medicine and Dentistry 29 51%
Social Sciences 6 11%
Nursing and Health Professions 4 7%
Psychology 2 4%
Agricultural and Biological Sciences 1 2%
Other 6 11%
Unknown 9 16%

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 13 April 2020.
All research outputs
#14,141,030
of 22,659,164 outputs
Outputs from BMC Medical Education
#1,943
of 3,291 outputs
Outputs of similar age
#152,798
of 238,843 outputs
Outputs of similar age from BMC Medical Education
#13
of 23 outputs
Altmetric has tracked 22,659,164 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,291 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. 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 238,843 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.