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Using the 3D Facial Norms Database to investigate craniofacial sexual dimorphism in healthy children, adolescents, and adults

Overview of attention for article published in Biology of Sex Differences, April 2016
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Title
Using the 3D Facial Norms Database to investigate craniofacial sexual dimorphism in healthy children, adolescents, and adults
Published in
Biology of Sex Differences, April 2016
DOI 10.1186/s13293-016-0076-8
Pubmed ID
Authors

Matthew J. Kesterke, Zachary D. Raffensperger, Carrie L. Heike, Michael L. Cunningham, Jacqueline T. Hecht, Chung How Kau, Nichole L. Nidey, Lina M. Moreno, George L. Wehby, Mary L. Marazita, Seth M. Weinberg

Abstract

Although craniofacial sex differences have been extensively studied in humans, relatively little is known about when various dimorphic features manifest during postnatal life. Using cross-sectional data derived from the 3D Facial Norms data repository, we tested for sexual dimorphism of craniofacial soft-tissue morphology at different ages. One thousand five hundred fifty-five individuals, pre-screened for craniofacial conditions, between 3 and 25 years of age were placed in to one of six age-defined categories: early childhood, late childhood, puberty, adolescence, young adult, and adult. At each age group, sex differences were tested by ANCOVA for 29 traditional soft-tissue anthropometric measurements collected from 3D facial scans. Additionally, sex differences in shape were tested using a geometric morphometric analysis of 24 3D facial landmarks. Significant (p < 0.05) sex differences were observed in every age group for measurements covering multiple aspects of the craniofacial complex. The magnitude of the dimorphism generally increased with age, with large spikes in the nasal, cranial, and facial measurements observed after puberty. Significant facial shape differences (p < 0.05) were also seen at each age, with some dimorphic features already present in young children (eye fissure inclination) and others emerging only after puberty (mandibular position). Several craniofacial soft-tissue sex differences were already present in the youngest age group studied, indicating that these differences emerged prior to 3 years of age. The results paint a complex and heterogeneous picture, with different groups of traits exhibiting distinct patterns of dimorphism during ontogeny. The definitive adult male and female facial shape was present following puberty, but arose from numerous distinct changes taking place at earlier stages.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 89 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 89 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 17%
Student > Master 11 12%
Student > Bachelor 10 11%
Student > Doctoral Student 6 7%
Researcher 6 7%
Other 14 16%
Unknown 27 30%
Readers by discipline Count As %
Medicine and Dentistry 25 28%
Agricultural and Biological Sciences 8 9%
Biochemistry, Genetics and Molecular Biology 4 4%
Unspecified 4 4%
Engineering 3 3%
Other 12 13%
Unknown 33 37%
Attention Score in Context

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 23 July 2023.
All research outputs
#15,201,366
of 24,567,524 outputs
Outputs from Biology of Sex Differences
#355
of 538 outputs
Outputs of similar age
#160,275
of 304,228 outputs
Outputs of similar age from Biology of Sex Differences
#6
of 8 outputs
Altmetric has tracked 24,567,524 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 538 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.4. This one is in the 31st percentile – i.e., 31% 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 304,228 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.