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Bayesian Networks Analysis of Malocclusion Data

Overview of attention for article published in Scientific Reports, November 2017
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11 Mendeley
Title
Bayesian Networks Analysis of Malocclusion Data
Published in
Scientific Reports, November 2017
DOI 10.1038/s41598-017-15293-w
Pubmed ID
Authors

Marco Scutari, Pietro Auconi, Guido Caldarelli, Lorenzo Franchi

Abstract

In this paper we use Bayesian networks to determine and visualise the interactions among various Class III malocclusion maxillofacial features during growth and treatment. We start from a sample of 143 patients characterised through a series of a maximum of 21 different craniofacial features. We estimate a network model from these data and we test its consistency by verifying some commonly accepted hypotheses on the evolution of these disharmonies by means of Bayesian statistics. We show that untreated subjects develop different Class III craniofacial growth patterns as compared to patients submitted to orthodontic treatment with rapid maxillary expansion and facemask therapy. Among treated patients the CoA segment (the maxillary length) and the ANB angle (the antero-posterior relation of the maxilla to the mandible) seem to be the skeletal subspaces that receive the main effect of the treatment.

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 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Other 3 27%
Professor 2 18%
Professor > Associate Professor 2 18%
Researcher 2 18%
Student > Ph. D. Student 1 9%
Other 1 9%
Readers by discipline Count As %
Unspecified 4 36%
Agricultural and Biological Sciences 3 27%
Physics and Astronomy 3 27%
Biochemistry, Genetics and Molecular Biology 1 9%

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 13 November 2017.
All research outputs
#7,308,322
of 9,896,588 outputs
Outputs from Scientific Reports
#31,371
of 47,177 outputs
Outputs of similar age
#175,916
of 267,292 outputs
Outputs of similar age from Scientific Reports
#3,372
of 5,903 outputs
Altmetric has tracked 9,896,588 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 47,177 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.3. This one is in the 28th percentile – i.e., 28% 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 267,292 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5,903 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.