Operationalizing Command Centre for COVID-19 Care Services in a Tertiary Care Teaching Hospital

COVID-19 was declared a public health emergency of international concern by WHO in March 2020. Hospitals were overburdened, health workers were drained and resources were depleting due to which people were desperately looking for hospital beds, medical oxygen and other necessities. For this purpose, a command centre was set up by the hospital administration department. The command centre targets for the enhancement of patient outcomes by coordination of care and centralized quality control. The main objectives of the command centre include internal communication between the departments concerned with COVID-19 care, appropriate resource allocation in the hospital for COVID-19 care and data compilation and dissemination of the real time data from COVID-19 war room to government organizations. The COVID-19 war room functions daily in a dedicated manner which helps in strategic planning and managing all the functions efficiently. The command centre though works dedicatedly, certain challenges are faced while carrying out the functions

All-epiphyseal headless screw fixation for Type III Paediatric Anterior Tibial Spine Fracture - A Case Report

Background: Paediatric anterior tibial spine fractures are rare and the management is controversial. Type III and IV anterior tibial spine fractures need absolute reduction and fixation using arthroscopic or open methods. We present a case of a Type III anterior tibial spine fracture fixed with open reduction and internal fixation with headless compression screws. Case report: A 12-year female child involved in a sports injury sustained a Type III anterior tibial spine fracture. This was managed with open reduction and intraepiphyseal fixation with headless compression screws using medial parapatellar approach. Protected weight bearing and knee range of motion exercises were encouraged after surgery. Fracture healed well at 4 months and the child returned to normal functional activity. At one year follow-up, the child attained a stable knee with full range of motion without any complication. Conclusion: Open reduction and intraepiphyseal fixation with headless compression screws is a viable option for Type III anterior tibial spine fractures and avoids growth disturbances by preserving the physis

The Roles of Privacy and Trust in Children’s Evaluations and Explanations of Digital Tracking

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/170227/1/cdev13572_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/170227/2/cdev13572.pd

Enhanced Preprocessing Approach Using Ensemble Machine Learning Algorithms for Detecting Liver Disease

There has been a sharp increase in liver disease globally, and many people are dying without even knowing that they have it. As a result of its limited symptoms, it is extremely difficult to detect liver disease until the very last stage. In the event of early detection, patients can begin treatment earlier, thereby saving their lives. It has become increasingly popular to use ensemble learning algorithms since they perform better than traditional machine learning algorithms. In this context, this paper proposes a novel architecture based on ensemble learning and enhanced preprocessing to predict liver disease using the Indian Liver Patient Dataset (ILPD). Six ensemble learning algorithms are applied to the ILPD, and their results are compared to those obtained with existing studies. The proposed model uses several data preprocessing methods, such as data balancing, feature scaling, and feature selection, to improve the accuracy with appropriate imputations. Multivariate imputation is applied to fill in missing values. On skewed columns, log1p transformation was applied, along with standardization, min–max scaling, maximum absolute scaling, and robust scaling techniques. The selection of features is carried out based on several methods including univariate selection, feature importance, and correlation matrix. These enhanced preprocessed data are trained on Gradient boosting, XGBoost, Bagging, Random Forest, Extra Tree, and Stacking ensemble learning algorithms. The results of the six models were compared with each other, as well as with the models used in other research works. The proposed model using extra tree classifier and random forest, outperformed the other methods with the highest testing accuracy of 91.82% and 86.06%, respectively, portraying our method as a real-world solution for detecting liver disease