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Purpose During the outbreak of Coronavirus disease of 2019 (COVID-19), the preparedness of emergency departments (EDs) for triaging of the patients and safety of staff is of utmost importance. The aim of our study was to develop and implement COVID-19 ED triage and protected intubation protocols for COVID-19 patients with in-situ simulation (ISS) training. The latent safety threats (LST) detection also served as a platform to test new system amendments and refine the protocols and workflows with infection control issues. We also explored the effectiveness of this approach based on Kirkpatrick’s model of evaluating training outcomes. Participants and Methods The protocols and simulation scenarios were developed and validated. A total of 22 triage and 13 intubation simulation sessions were conducted in the ED with multidisciplinary staff (physicians=18, nurses=20) during a period of four months. Each simulation was followed by a debriefing session to discuss the team performance. Pre- and post-simulation performances were compared. LSTs were identified and remediated. An online voluntary feedback was collected from the participants to explore the opinion about the ISS sessions and confidence level using a 5-point Likert scale. Results There was a significant improvement in triage knowledge score after ISS [5.5/10 (IQR 4–6) versus 8.5/10 (IQR 8–9), p75%) following the ISS for triage case scenarios. A pre-designed checklist was used during protective intubation simulations. Some important LSTs were missing medications, lack of mechanism to deliver patient samples to lab and faulty airway maneuvers. The participants’ feedback on ISS showed increased skills and confidence level on triaging and protected intubation (p

Introduction Patient education is of paramount importance with regard to the condition of the disease and the treatment given besides lifestyle remodelling in order to get the desired therapeutic outcomes. When verbal information is provided to the patients, they often tend to forget it. Pictorial aids or pictograms, as they are commonly known, are tools that are widely used for imparting knowledge to the patients. The aim of the study is to prepare and validate a Pictogram-based Patient Information Leaflet (P-PILs) on Tuberculosis (TB). Methods P-PILs have been prepared from tertiary, secondary and primary sources. The knowledge-based questions are prepared with respect to the P-PILs. The baseline knowledge of the volunteers and patients has been analyzed before administering the P-PILs by using the validated questionnaire. The post-knowledge of the volunteers and patients has been analyzed after administering the P-PILs (20 minutes) by using the same questionnaire and the user-opinion has also been obtained at the end. Results The study results show that the mean scores of the overall user-testing knowledge assessment are found to have improved significantly from the pre-P-PILs administration score of 62.67 to the post-P-PILs administration score of 91. The overall user-opinion about the P-PILs has been found to be good (75%) followed by average (25%). Conclusion The present study shows that there is significant improvement in the knowledge levels of the patients and volunteers after reading the validated leaflets. The P-PILs are found to be an effective educational tool for TB patients.

Many machine learning algorithms have been used to classify pixels in Landsat imagery. The maximum likelihood classifier is the widely-accepted classifier. Non-parametric methods of classification include neural networks and decision trees. In this research work, we implemented decision trees using the C4.5 algorithm to classify pixels of a scene from Juneau, Alaska area obtained with Landsat 8, Operation Land Imager (OLI). One of the concerns with decision trees is that they are often over fitted with training set data, which yields less accuracy in classifying unknown data. To study the effect of overfitting, we have considered noisy training set data and built decision trees using randomly-selected training samples with variable sample sizes. One of the ways to overcome the overfitting problem is pruning a decision tree. We have generated pruned trees with data sets of various sizes and compared the accuracy obtained with pruned trees to the accuracy obtained with full decision trees. Furthermore, we extracted knowledge regarding classification rules from the pruned tree. To validate the rules, we built a fuzzy inference system (FIS) and reclassified the dataset. In designing the FIS, we used threshold values obtained from extracted rules to define input membership functions and used the extracted rules as the rule-base. The classification results obtained from decision trees and the FIS are evaluated using the overall accuracy obtained from the confusion matrix.

Background and Aims : Rheumatic Fever, Rheumatic Heart Disease and Congenital Heart disease are the most common cardiac problems in school children. Prior studies have reported different prevalence rate of Rheumatic Heart Disease among different groups of population of Nepal. The aim of this study was to estimate the prevalence of Rheumatic Fever, Rheumatic Heart Disease and Congenital Heart Disease among school children in Kathmandu Valley of Nepal. Methods: Cardiac screening of 34,876 school children from 115 randomly selected public schools from two cities of Kathmandu Valley (Kathmandu and Lalitpur) was done. Cases with abnormal findings in auscultation underwent echocardiography and the diagnosis was confirmed. Results: The prevalence of Congenital Heart Disease was noted to be 1 per thousand and prevalence of Rheumatic Heart Disease was found to be 0.90 per thousand (in the age group 5-16 years) with the most common lesion being Mitral Regurgitation. No significant statistical difference was noted between male and female students in both the cases of Rheumatic Heart Disease and Congenital Heart Disease. No cases of Acute Rheumatic Fever were noted. Conclusion: The prevalence of Rheumatic Heart Disease among school children in Kathmandu valley was noted to be lower than reported in similar previous studies. Primary and secondary prevention programs of RF/RHD have been effective in Nepal and are needed to be strengthened and expanded to further reduce the burden of these diseases. Nepalese Heart Journal | Volume 10 | No.1 | November 2013| Pages 1-5 DOI: http://dx.doi.org/10.3126/njh.v10i1.9738