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Background: The aim of this study was to assess whether the Harris Hip Score (HHS) and the Oxford Hip Score (OHS) were comparable in normal, healthy, pathology-free individuals of different age, gender, ethnicity, handedness and nationality. The purpose of this study was to establish normal population values for the HHS and OHS using an electronic data collection system.
Methods: 317 Australian and 310 Canadian citizens with no active hip pain, injury or pathology in the ipsilateral hip corresponding to their dominant arm, were evaluated. Participants completed an electronically-administered questionnaire and were assessed clinically. Chi-square tests, Fisher's exact test and Poisson regression models were used where appropriate, to investigate the association between hip scores, ethnicity, nationality, gender, handedness and age.
Results: There was a statistically significant association between the OHS and age (p<0.0001) and the HHS and age (p = 0.0006); demonstrating that as age increased, normal hip scores decreased. There was no statistically significant association between the HHS and gender (p = 0.1389); or HSS and nationality, adjusting for age (p = 0.5698) and adjusting for gender (p = 0.6997). There was no statistically significant association between the OHS and gender (p = 0.1350). Australians reported a statistically significant 4.2% higher overall OHS value compared to Canadians (p = 0.0490). There was no statistically significant association between the OHS and nationality in age groups 18-79 years. Participants >80 years reported a statistically significant association between the OHS and nationality (p<0.0001).
Conclusions: Studies using an electronic control group should consider differences in gender, age, ethnicity and nationality when using the HHS and OHS to assess patient outcomes. This study has established an electronic, normal control group for studies using the HHS and OHS. When using the OHS, the control group should be sourced from the same country of origin. When using the HHS, the control group should be sourced from a pre-established control group within a database, without necessarily being sourced from the same country of origin.
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http://dx.doi.org/10.5301/hipint.5000465 | DOI Listing |
Reprod Biol
September 2025
Department of Obstetrics and Gynecology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Engineering Research Center of Biopreservation and Artificial Organs, Ministry of Education, No 218 Jixi Road, Hefei Anhui230022, China; Key Laboratory of Population Health Across
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October 2025
Department of Radiology, Mayo Clinic, Rochester, MN.
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Phys Rev Lett
August 2025
California Institute of Technology, TAPIR, Division of Physics, Mathematics, and Astronomy, Pasadena, California 91125, USA.
In the gravitational-wave analysis of pulsar-timing-array datasets, parameter estimation is usually performed using Markov chain Monte Carlo methods to explore posterior probability densities. We introduce an alternative procedure that instead relies on stochastic gradient-descent Bayesian variational inference, whereby we obtain the weights of a neural-network-based approximation of the posterior by minimizing the Kullback-Leibler divergence of the approximation from the exact posterior. This technique is distinct from simulation-based inference with normalizing flows since we train the network for a single dataset, rather than the population of all possible datasets, and we require the computation of the data likelihood and its gradient.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Pathology, Boston Children's Hospital, Harvard School of Medicine, Boston, Massachusetts, United States of America.
The Sudden Infant Death Syndrome (SIDS) is a major global health problem, with increased risk among socioeconomically disadvantaged populations. We propose SIDS, or a subset, is due to a defect in the brainstem serotonin system mediating cardiorespiratory integration and arousal. This defect impinges on homeostasis during a critical developmental period in infancy, especially in populations experiencing maternal and infantile stress, resulting in sleep-related sudden death.
View Article and Find Full Text PDFAnn Acad Med Singap
August 2025
Dementia Research Centre (Singapore), Lee Kong Chian School of Medicine, Nanyang Technology University, Singapore.
Introduction: Interpretation and analysis of magnetic resonance imaging (MRI) scans in clinical settings comprise time-consuming visual ratings and complex neuroimage processing that require trained professionals. To combat these challenges, artificial intelligence (AI) techniques can aid clinicians in interpreting brain MRI for accurate diagnosis of neurodegenerative diseases but they require extensive validation. Thus, the aim of this study was to validate the use of AI-based AQUA (Neurophet Inc.
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