ABSTRACT
BACKGROUND: Endotracheal intubation and mechanical ventilation in individuals experiencing acute exacerbations of chronic obstructive pulmonary disease (COPD) are associated with several complications. Therefore, utilizing noninvasive positive pressure ventilation (NIPPV) is the suggested initial management for these individuals. The current study was done to assess and compare the clinical and physiological parameters before and after the application of NIPPV and also to evaluate the outcomes of NIPPV. METHODOLOGY: A prospective observational study was conducted on 50 patients with COPD experiencing acute exacerbations. These patients were treated with NIPPV. Measurements of blood pressure, respiratory rate (RR), heart rate (HR), dyspnea using the modified Borg scale, and arterial blood gas (ABG) parameters (pH, PaCO2, and PaO2) were recorded at baseline, one hour, six hours, 24 hours, and daily until discharge. The study's outcomes included the subjects who successfully underwent NIPPV and failed during NIPPV. RESULTS: NIPPV effectively reduced the dyspnea score from 7.24 ± 1.58 at baseline to 5.53 ± 1.82 at one hour, 4.11 ± 1.75 at six hours, 2.60 ± 1.03 at 24 hours, and 1.26 ± 0.44 at the time of discharge. Significant improvements were also observed in HR and RR (P < 0.001). When compared to the baseline, the pH level was significantly maintained, PaCO2 was decreased, and PaO2 was increased at various times. Mortality was observed in four patients. CONCLUSIONS: NIPPV was successful in 42 (84%) patients, with improvements in ABG and pH for early recovery and reduced hospital stay.
ABSTRACT
Mobile learning has been increased in past years and has attracted the interests of academicians and educators in the past many years especially in higher education. The mobile-based online test is the buzzing in the current pandemic time. Institutions need to use online learning as a powerful tool for conducting exams and assess the students effectively. Integrating technology in education can be advantageous for universities and help engage better results for students. Therefore, it is important to understand each student their capacities and create a different test based on the required difficulty. Students should be graded based on their capabilities. The purpose of the research study is to develop the progressive model with the calibration of difficulty level according to the student capacity. To achieve the goal, a test of 20 python questions was conducted on 120 students with each question having difficulty given by 8 field experts. To verify the model, 5 categories were formed with different difficulty levels which in turn gave satisfactory results. To find a relation between the initial difficulty and the calculative difficulty based on the student response, a correlation test was conducted. After careful analysis of the question difficulty and student responses, it was observed that both are highly dependent on each other wherein the difficulty level of any question can be calculated using incorrect answers. The correlation coefficient obtained between them was 0.9833. Upon collecting the difficulty of the questions and student responses, respective grading could be done using the stated formula. Later on, the progressive model was simulated with five different cases (Best case, above-average case, below average case, the average case, worst case). The model outperformed in all the cases with appropriate difficulty levels. Online Tests have ushered a revolution in the assessment of students but yet they tend to be unpopular in India as the evaluation based on pen-paper approach is preferred. The main reasons for this are difficult to grade everyone at the same level, susceptible to cheating, and transition to open books. Using our study, universities can identify obstacles, and prepare an appropriate result-driven plan of action for implementing the mobile-based online test and make easy migration from paper-based test to online test.
