Measurement of cervical range of motion using smartphone compass in the healthy people:a cross-sectional pilot study

Objective:This study evaluates the reliability of smartphone compass software in measuring the cervical range of motion in healthy people.Methods:We selected 40 healthy intern college students from Tianjin Hospital from June to August 2022 to participate in this study.Two physiotherapists used a smartphone(iPhone 11256 Gb(model A2223))compass software to measure six directions of motion of the cervical spine in 40 subjects in a total of 3 rounds each.The intraclass correlation coefficient was used to compare the reliability intra-group,and the Pearson correlation coefficient was also used to compare the correlation between groups,with P<0.05 being statistically significant.Results:The intraclass correlation coefficient showed good reliability(>0.5)in cervical range of motion(CROM),especially in cervical flexion and right rotation(>0.9).In the correlation comparison between the two groups,the Spearman comparison was used,and the six directions of the cervical spine were significantly correlated(P<0.05).Conclusion:The built-in compass software in smartphones has good reliability in measuring CROM in healthy people.

Acquisition repeatability of MRI radiomics features in the head and neck:a dual-3D-sequence multi-scan study

Radiomics has increasingly been investigated as a potential biomarker in quantitative imaging to facilitate personalized diagnosis and treatment of head and neck cancer(HNC),a group of malignancies associated with high heterogeneity.However,the feature reliability of radiomics is a major obstacle to its broad validity and generality in application to the highly heterogeneous head and neck(HN)tissues.In particular,feature repeatability of radiomics in magnetic resonance imaging(MRI)acquisition,which is considered a crucial confounding factor of radiomics feature reliability,is still sparsely investigated.This study prospectively investigated the acquisition repeatability of 93 MRI radiomics features in ten HN tissues of 15 healthy volunteers,aiming for potential magnetic resonance-guided radiotherapy(MRgRT)treatment of HNC.Each subject underwent four MRI acquisitions with MRgRT treatment position and immobilization using two pulse sequences of 3D T1-weighed turbo spin-echo and 3D T2-weighed turbo spin-echo on a 1.5T MRI simulator.The repeatability of radiomics feature acquisition was evaluated in terms of the intraclass correlation coefficient(ICC),whereas within-subject acquisition variability was evaluated in terms of the coefficient of variation(CV).The results showed that MRI radiomics features exhibited heterogeneous acquisition variability and uncertainty dependent on feature types,tissues,and pulse sequences.Only a small fraction of features showed excellent acquisition repeatability(ICC>0.9)and low within-subject variability.Multiple MRI scans improved the accuracy and confidence of the identification of reliable features concerning MRI acquisition compared to simple test-retest repeated scans.This study contributes to the literature on the reliability of radiomics features with respect to MRI acquisition and the selection of reliable radiomics features for use in modeling in future HNC MRgRT applications.

Predictors of the Aggregate of COVID-19 Cases and Its Case-Fatality: A Global Investigation Involving 120 Countries

Objective: Since the identification of COVID-19 in December 2019 as a pandemic, over 4500 research papers were published with the term “COVID-19” contained in its title. Many of these reports on the COVID-19 pandemic suggested that the coronavirus was associated with more serious chronic diseases and mortality particularly in patients with chronic diseases regardless of country and age. Therefore, there is a need to understand how common comorbidities and other factors are associated with the risk of death due to COVID-19 infection. Our investigation aims at exploring this relationship. Specifically, our analysis aimed to explore the relationship between the total number of COVID-19 cases and mortality associated with COVID-19 infection accounting for other risk factors. Methods: Due to the presence of over dispersion, the Negative Binomial Regression is used to model the aggregate number of COVID-19 cases. Case-fatality associated with this infection is modeled as an outcome variable using machine learning predictive multivariable regression. The data we used are the COVID-19 cases and associated deaths from the start of the pandemic up to December 02-2020, the day Pfizer was granted approval for their new COVID-19 vaccine. Results: Our analysis found significant regional variation in case fatality. Moreover, the aggregate number of cases had several risk factors including chronic kidney disease, population density and the percentage of gross domestic product spent on healthcare. The Conclusions: There are important regional variations in COVID-19 case fatality. We identified three factors to be significantly correlated with case fatality.

Objective Bayesian hypothesis testing and estimation for the intraclass model

The intraclass correlation coefficient (ICC) plays an important role in various fields of study asa coefficient of reliability. In this paper, we consider objective Bayesian analysis for the ICCin the context of normal linear regression model. We first derive two objective priors for theunknown parameters and show that both result in proper posterior distributions. Within aBayesian decision-theoretic framework, we then propose an objective Bayesian solution to theproblems of hypothesis testing and point estimation of the ICC based on a combined use of theintrinsic discrepancy loss function and objective priors. The proposed solution has an appealinginvariance property under one-to-one reparametrisation of the quantity of interest. Simulationstudies are conducted to investigate the performance the proposed solution. Finally, a real dataapplication is provided for illustrative purposes.