Assessing the Transfer of Nursing Competencies: Analysis of the effects of intermediate care training in a hospital setting

This research focuses on evaluating the effectiveness of a training course in a hospital setting, using indicators of effect size and heterogeneity index. The evaluation focused on a training course in intermediate care for nurses. The course lasted 23 days and included clinical teaching at the patient’s bedside. The competencies were measured at the beginning and end of the training course, using an observation grid based on five domains: Clinical Expert, Communicator, Collaborator, Leader and Learner-Trainer. Cohen’s and Glass’s estimators demonstrated significant effects of training on the five domains while the heterogeneity index showed a reduction in behavioural disparities within the nursing group at the end of the training course. The discussion addresses issues relating to the boundaries used to interpret effect sizes.

DDA Simulation Study on Mechanical Failure of Heterogenous Rock

Heterogeneity is an important characteristic that affects the mechanical behavior of rock. In the present work, a statistical rock mesoheterogeneity model based on the Weibull distribution function is introduced into the discontinuous deformation analysis (DDA) method to simulate the mechanical failure of heterogeneous rock, in which the general heterogeneity degree is controlled by a heterogeneity index and the mechanical property of each subblock element is randomly assigned. Brazilian disc and uniaxial compressive rectangular specimens are simulated as examples. Results show that it is more reasonable to consider the heterogeneity of elasticity properties (the elastic modulus and Poisson’s ratio) and strength properties (the tensile strength, cohesion, and friction angle) simultaneously in the heterogeneity model. It is also shown that with a larger heterogeneity index, which means a lower degree of heterogeneity, the reproducibility of the macroscopic response curves of a specimen gets better, while the exact cracking always differs but with less scattered cracks, and the global fracturing failure pattern and mode are weakly influenced by the heterogeneity. Moreover, with the increase in the heterogeneity index, the macroscopic equivalent modulus and strength get larger and approach those of a homogeneous specimen. This work indicates the importance of heterogeneity for rock mechanical behaviors including the macroscopic equivalent response and the fracturing failure. By the subblock DDA method to simulate fracturing realistically, the fracturing failure process of heterogeneous rock can be successfully reproduced, which builds good foundation for the simulation study of heterogeneous rock fracturing in practical problems, e.g., coal and rock fracturing in fluidization mining in the future.

Machine Learning Models for COVID-19 Confirmed Cases Prediction: A Meta-Analysis Approach

COVID-19, CoronaVirus Disease – 2019, belongs to the genus of Coronaviridae. COVID-19 is no longer pandemic but rather endemic with the number of deaths around the world of more than 3,166,516 cases. This reality has placed a massive burden on limited healthcare systems. Thus, many researchers try to develop a prediction model to further understand this phenomenon. One of the recent methods used is machine learning models that learn from the historical data and make predictions about the events. These data mining techniques have been used to predict the number of confirmed cases of COVID-19. This paper investigated the variability of the effect size on the correlation performance of machine learning models in predicting confirmed cases of COVID-19 using meta-analysis. It explored the correlation between actual and predicted COVID-19 cases from different Neural Network machine learning models by means of estimated variance, chi-square heterogeneity (Q), heterogeneity index (I2) and random effect model. The results gave a good summary effect of 95% confidence interval. Based on chi-square heterogeneity (Q) and heterogeneity index (I2), it was found that the correlations were heterogeneous among the studies. The 95% confidence interval of effect summary also supported the difference in correlation between actual and predicted number of confirmed COVID-19 cases among the studies. There was no evidence of publication bias based on funnel plot and Egger and Begg’s test. Hence, findings from this study provide evidence of good prediction performance from the Neural Network model based on a combination of studies that can later serve in the prediction of COVID-19 confirmed cases.

The Effect of Chemical Parameters on Water Quality Index in Machine Learning Studies: A Meta-Analysis

According to the World Health Organization (WHO), approximately 2 billion people worldwide use drinking water sources that are contaminated with faeces. This is a serious issue since contaminated water may lead to certain waterborne diseases such as cholera, hepatitis A, dysentery, jaundice, and typhoid fever. Therefore, many researchers around the world are interested in studying the water quality. One of the most commonly used approaches is by using machine learning. Machine learning approach has grabbed the interest of many researchers since the last several years due to its power to compute complicated mathematical computations on big data analysis. Therefore, this study explored the correlation between different water quality parameters and Water Quality Index (WQI) in water quality studies that used machine learning by using a meta-analysis approach. This study used estimated variance, heterogeneity index, Chi-squared heterogeneity test and the random effects model. Based on the selected articles, pH, dissolved oxygen (DO) and biochemical oxygen demand (BOD) are the parameters commonly used in water quality studies which use a machine learning approach. This study found that pH is the best chemical factor which greatly affects the Water Quality Index since it has the highest mean correlation and lowest estimated variance due to sampling error. The result showed that the correlation between pH and WQI are heterogeneous across studies based on the Chi-squared of heterogeneity, Q and heterogeneity index, I2 value. The 95% confidence interval of effect summary supports the findings that the correlation of pH is different among the studies. This study also found that there is no evidence of publication bias using Egger and Begg’s test. Therefore, in order to ensure good water quality supply, the local authorities and government agencies should give more attention to this parameter since pH of water plays an important role in determining the water quality status.

The Role of Simultaneous Standardized Uptake Value and MRI Diffusion Weighted Heterogeneity Index from Hybrid PET/MR in the Evaluation of Brain Metastases

BackgroundPET/MRI is a hybrid imaging modality and uses for evaluating oncology patients with its benefits of combination of soft tissue contrast and glucose metabolism. AimsTo evaluate the role of simultaneous derived apparent diffusion coefficient (ADC) heterogeneity index and 2-[18F]-fluoro-2-deoxy-D-glucose (18F-FDG) standardized uptake value (SUV) from hybrid PET/MRI to differentiate brain metastasis (BM) from normal appearing brain parenchyma (NABP) and to find out the efficiency of combination of ADCCV and SUV against conventional ADC parameter. Study DesignWhole-body PET/MRI was performed to evaluate proven 49 BM of 26 oncology patients (15 females, 11 males; mean age 63±16 years), sourced from different primary cancer. MethodsBrain sequences, which were dixon and diffusion weighted imaging (DWI) protocols with simultaneous PET were used to calculate coefficient of variance of the ADC (ADCCV) and SUVmax. All images were assessed by three radiologists and the same size of VOI was placed on BM and NABP. Inter-rater reliability was tested by inter-class correlation (ICC). The correlation of ADCCV and SUVmax and the differences in ADC values and SUVmax between BM and NABP were investigated. ResultsThe excellent consistency was found between raters at ADCmean (0.972) and ADCCV (0.995). There was a moderate correlation between ADCCV and SUVmax (r=0.585) and a negligible inverse correlation between ADCmean and SUVmax (r=-0.154). A statistically significant difference between BM and NABP was determined for ADCCV (p<0.001) and SUVmax (p=0.007). An area under the curve (AUC) of 0.663, 0.966, 0.571, 0.696 and 0.971 were obtained with ROC analysis of SUVmax, ADCCV, ADCmean, SUVmax+ADCmean and SUVmax+ADCCV, respectively.ConclusionADCCV may be considered as a new parameter in multi-parametric MRI that quantitatively discriminates BM from NABP with excellent interrater reliability.

Diagnostic Performance of MRI Diffusion Weighted Heterogeneity Index to Discriminate Brain Metastases from Normal Brain Tissue in Hybrid PET/MR

IntroductionTo evaluate diagnostic performance of apparent diffusion coefficient (ADC) heterogeneity index to differentiate brain metastasis (BM) from normal appearing brain parenchyma (NABP) and to find out the correlation between 2-[18F]-fluoro-2-deoxy-D-glucose (18F-FDG) standardized uptake value (SUV) and ADC heterogeneity index derived from hybrid PET/MRI.MethodsWhole-body PET/MRI was performed to evaluate proven 40 BM of 18 oncology patients (9 females, 9 males; mean age 61±16 years), sourced from different primary cancer. Brain sequences, which were dixon and diffusion weighted imaging (DWI) protocols with simultaneous PET were used to calculate coefficient of variance of the ADC (ADCCV) and SUVmax. All images were assessed by three radiologists and the same size of VOI was placed on BM and NABP. Inter-rater reliability was tested by inter-class correlation (ICC). The correlation of ADCCV and SUVmax and the differences in ADC values and SUVmax between BM and NABP were investigated.ResultsThe excellent consistency was found between raters at ADCmean (0.972) and ADCCV (0.995). There was a strong correlation between ADCCV and SUVmax (r=0.763) and a slight inverse correlation between ADCmean and SUVmax (r=-0.122). A statistically significant difference between BM and NABP was determined for ADCCV (p<0.001) and SUVmax (p<0.001). An area under the curve (AUC) of 0.960, 0.998 and 0.574 were obtained with ROC analysis of SUVmax, ADCCV and ADCmean, respectively.ConclusionADCCV may be considered as a potential biomarker that quantitatively discriminates BM from NABP with excellent interrater reliability.

Impact of antibiotic heterogeneity by periodic antibiotic monitoring and supervision strategy at two units with different prevalences of multidrug-resistant organisms

In an intensive care unit, antibiotic heterogeneity led to an increase in antibiotic heterogeneity index (P = .002) and a reduction in carbapenem-resistance Enterobacteriaceae incidence (P = .04). In a general medicine unit with low prevalence of multidrug-resistant organisms, antibiotic heterogeneity index and incidence of multidrug-resistant organisms did not improve.