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Prediction of fracture load and stiffness of the proximal femur by CT-based specimen specific finite element analysis: cadaveric validation study

Overview of attention for article published in BMC Musculoskeletal Disorders, December 2017
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Title
Prediction of fracture load and stiffness of the proximal femur by CT-based specimen specific finite element analysis: cadaveric validation study
Published in
BMC Musculoskeletal Disorders, December 2017
DOI 10.1186/s12891-017-1898-1
Pubmed ID
Authors

Michiaki Miura, Junichi Nakamura, Yusuke Matsuura, Yasushi Wako, Takane Suzuki, Shigeo Hagiwara, Sumihisa Orita, Kazuhide Inage, Yuya Kawarai, Masahiko Sugano, Kento Nawata, Seiji Ohtori

Abstract

Finite element analysis (FEA) of the proximal femur has been previously validated with large mesh size, but these were insufficient to simulate the model with small implants in recent studies. This study aimed to validate the proximal femoral computed tomography (CT)-based specimen-specific FEA model with smaller mesh size using fresh frozen cadavers. Twenty proximal femora from 10 cadavers (mean age, 87.1 years) were examined. CT was performed on all specimens with a calibration phantom. Nonlinear FEA prediction with stance configuration was performed using Mechanical Finder (mesh,1.5 mm tetrahedral elements; shell thickness, 0.2 mm; Poisson's coefficient, 0.3), in comparison with mechanical testing. Force was applied at a fixed vertical displacement rate, and the magnitude of the applied load and displacement were continuously recorded. The fracture load and stiffness were calculated from force-displacement curve, and the correlation between mechanical testing and FEA prediction was examined. A pilot study with one femur revealed that the equations proposed by Keller for vertebra were the most reproducible for calculating Young's modulus and the yield stress of elements of the proximal femur. There was a good linear correlation between fracture loads of mechanical testing and FEA prediction (R2 = 0.6187) and between the stiffness of mechanical testing and FEA prediction (R2 = 0.5499). There was a good linear correlation between fracture load and stiffness (R2 = 0.6345) in mechanical testing and an excellent correlation between these (R2 = 0.9240) in FEA prediction. CT-based specimen-specific FEA model of the proximal femur with small element size was validated using fresh frozen cadavers. The equations proposed by Keller for vertebra were found to be the most reproducible for the proximal femur in elderly people.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 76 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 18%
Student > Bachelor 10 13%
Researcher 9 12%
Student > Master 7 9%
Student > Doctoral Student 3 4%
Other 8 11%
Unknown 25 33%
Readers by discipline Count As %
Engineering 28 37%
Medicine and Dentistry 9 12%
Nursing and Health Professions 3 4%
Materials Science 2 3%
Biochemistry, Genetics and Molecular Biology 1 1%
Other 3 4%
Unknown 30 39%
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 11 July 2018.
All research outputs
#17,982,872
of 23,094,276 outputs
Outputs from BMC Musculoskeletal Disorders
#2,950
of 4,109 outputs
Outputs of similar age
#308,211
of 440,460 outputs
Outputs of similar age from BMC Musculoskeletal Disorders
#65
of 93 outputs
Altmetric has tracked 23,094,276 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,109 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.1. This one is in the 22nd percentile – i.e., 22% 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 440,460 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 93 others from the same source and published within six weeks on either side of this one. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.