Objective: This study aims to evaluate the assumption of geometric similitude inherent to equal-stress equal-velocity scaling by determining if scale factors created with different anthropometry metrics result in different scaled injury tolerance predictions. This assumption will be evaluated when equal-stress equal-velocity scaling is employed across dissimilar (e.g., 50 male to small female) and similar (e.g., small female to a reference small female anthropometry) anthropometries.
Methods: Three average male and three small female lower extremity specimens that were tested in ankle inversion/eversion were selected for scaling analysis. Three additional female specimens were selected as a reference dataset, such that the accuracy of the scaled data could be compared to an independent measured dataset. The failure moments, total height and total weight for these donors were determined from literature. Additional anthropometry metrics (leg length, calcaneus height, and bimalleolar width) were taken from each of their respective CT scans. Scale factors were calculated from these previously determined anthropometric metrics for the six donors selected for scaling analysis by targeting the averaged anthropometry metrics of the reference small female dataset. Equal-stress equal-velocity scaling was applied to the failure moments from literature using different scale factors. The mean predicted failure tolerance and standard deviation for scaled data using different scale factors were compared to one another and to the mean failure tolerance from the reference (unscaled) small female dataset.
Results: When using average male data to predict ankle failure moment for a small female anthropometry, scaled moments were statistically significantly different from measured small female failure moment. Furthermore, scaled failure moments predicted using scale factors based on different anthropometry metrics were found to be significantly different from one another. Conversely, predicted mean failure moment using scaled female data of a similar size to the reference data was not significantly different from measured female failure moment, and the predicted failure moments were not significantly affected by choice of scale factor.
Conclusions: This study shows that an injury metric predicted with equal-stress equal-velocity scaling is sensitive to choice of scale factor when employing scaling across occupants of dissimilar size and sex. This conclusion suggests error can be introduced into scaled response due to choice of anthropometry metric used to create a scale factor, and therefore, anthropometry metrics used to create scale factors should be justified mechanistically and shown to apply across size and sex before being employed.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1080/15389588.2020.1829919 | DOI Listing |
Genet Sel Evol
January 2025
GenPhySE, Université de Toulouse, INRAE, ENVT, 31326, Castanet-Tolosan, France.
Background: The magnitude of inbreeding depression depends on the recessive burden of the individual, which can be traced back to the hidden (recessive) inbreeding load among ancestors. However, these ancestors carry different alleles at potentially deleterious loci and therefore there is individual variability of this inbreeding load. Estimation of the additive genetic value for inbreeding load is possible using a decomposition of inbreeding in partial inbreeding components due to ancestors.
View Article and Find Full Text PDFActa Neurochir (Wien)
January 2025
Division of Neuroradiology and Joint Department of Medical Imaging, University Health Network and Toronto Western Hospital, Toronto, ON, M5T 2S8, Canada.
Purpose: It was noticed that anterior choroidal artery (AChoA) aneurysms appear to rupture at relatively smaller sizes compared with aneurysms in other intracranial locations, based on anecdotal clinical experience. We therefore aimed to compare ruptured AChoA aneurysms with other ruptured aneurysms in other intracranial locations, pertaining to aneurysm dimensions. This may help in finding out if the rupture risk stratification, based on the amalgamation of aneurysms of multiple locations in one group, precisely estimates aneurysm rupture risk.
View Article and Find Full Text PDFCMAJ
January 2025
Schools of Health and Wellbeing (Nakada, Pell, Ho), and Cardiovascular and Metabolic Health (Welsh, Celis-Morales), University of Glasgow, Glasgow, UK; Human Performance Laboratory, Education, Physical Activity and Health Research Unit (Celis-Morales), Universidad Católica del Maule, Talca, Chile; Centro de Investigación en Medicina de Altura (CEIMA) (Celis-Morales), Universidad Arturo Prat, Iquique, Chile.
Background: Anxiety and depression are associated with cardiovascular disease (CVD). We aimed to investigate whether adding measures of anxiety and depression to the American Heart Association Predicting Risk of Cardiovascular Disease Events (PREVENT) predictors improves the prediction of CVD risk.
Methods: We developed and internally validated risk prediction models using 60% and 40% of the cohort data from the UK Biobank, respectively.
Clin Teach
February 2025
Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA.
Background: Seeking to provide early paediatric nephrology exposure to medical students in the United States, we implemented the Kids In Dialysis, Nephrology Exposure and Education (KIDNEE) club. This club served as an educational intervention in which preclinical medical students were paired with paediatric dialysis patients, as patient buddies.
Approach: Students were recruited for involvement in the club through the medical school Paediatric Interest Group.
J Affect Disord
January 2025
University of Otago, New Zealand. Electronic address:
Background: Stress is a major public health issue linked to physical and mental health disorders, economic burdens, and social challenges. Understanding its prevalence and determinants across demographic and economic groups is essential for effective intervention.
Methods: This study uses data from the Gallup World Poll, with over 300,000 participants across 131 countries.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!