The effects of the Covid-19 pandemic on the performance of academic journals: The case of Turkey

Since the beginning of 2020, "Covid-19” has affected the whole world in an unprecedented way in modern times. It is inevitable that this pandemic, which has negatively affected many fields, will also have an impact on academic journals. The aim of this study is to determine the effect of the Covid-19 pandemic on the performance of academic journals. In our study, a "Data Envelopment Analysis” methodology with 3 inputs and 3 outputs was used to determine the relative "performance of the journals”. Within the scope of the study, 109 journals published in "Turkey” and scanned in "Web of Science” indexes were examined. Results show that eleven journals were efficient in 2019, while in 2020 this number decreased to seven. Four fields have been positively affected by the pandemic and journals publishing in these fields have increased their efficiencies. Eighteen fields were adversely affected by the pandemic and the efficiency of journal publishing in these fields decreased. Eleven fields and journals publishing in these fields maintained their efficiency both before and during the pandemic. As the Covid-19 pandemic is not over yet, our data is limited. In the coming years, more detailed and comprehensive studies can be carried out with more extensive data and a further number of journals from different countries. © 2022 Pontificia Universidade Catolica de Campinas. All rights reserved.

Analyzing Post-Covid-19 Teaching Methods in Higher Education: A Quality and Experience Based Decision-Making Study

As the world suffers from the Covid-19 pandemic for more than a year, a new way of life has begun for people in their professional as well as private lives. Therefore, previous methods, habits or procedures during the pandemic may no longer be valid. Education, being one of the most affected sectors during this period, together with its broad related environment have been significantly impacted. In this context, the present study focused on higher education. Thus, the aim of this study was to assess the different teaching methods after the Covid-19 pandemic period from the point of view of lecturers working in the health services department of a state university in Turkey. Accordingly, two hierarchical models: service quality and experience based were developed and the opinions of lecturers were obtained using one of the multi criteria decision-making (MCDM) methods, namely the Analytic Hierarchy Process (AHP). Face-to-face was found to be the optimum teaching method for both the models while the rest of the teaching alternatives were ranked separately in order of importance for these two models. Moreover, criteria were prioritized for the first and the second models, respectively. Limitations of the study including future research directions were identified.

Early Detection of Mortality in COVID-19 Patients Through Laboratory Findings with Factor Analysis and Artificial Neural Networks

In this paper, some biochemical findings of patients who applied to Kocaeli University Faculty of Medicine Emergency Service with suspicion of COVID-19 are exam-ined. The common characteristics of the cases regarding mortality status are analyzed via factor analysis (FA). Following the FA, blood parameters related to the severity of the cases are determined. Finally, a multi-layered artificial neural network (ANN) is trained with these parameters. This paper proposes a method that helps early detection of severe cases and determination of non-risk group vaccination priority. Thus, the main contribution is the creation of a decision-support system to start advanced medical support as soon as possible. The data set consists of 105 patients with 19 different input parameters. After FA, 7 parameters are found relevant to one-month mortality. These are HB, AST, BUN, LDH, pH, HCO3 and LAC. The chi-square value was 1252.9552, the p value for the significance level of 0.05 was close to zero (7.3696x10−156 ). An ANN is accurately trained based on this subset of the data. The most successful model of ANN’s training and testing errors as a root sum squared estimate of error (RSSE) are 0.1958 and 0.2402, respectively. This ANN model can be queried for patient data with determined parameters. This paper shows that the early detection of patients who can have the severe or fatal disease can be determined regarding COVID-19. The proposed method can be used to determine vaccination priority, for early intervention to expected severe course of treatment, and medical analysis and analytics of unknown diseases via their outcomes, enriched with numerical laboratory test results. © 2022, Publishing House of the Romanian Academy. All rights reserved.

Prediction of mortality, hospitalization and mechanical ventilation needs of patients with pneumonia in COVID-19 outbreak

Objective: The COVID-19 pandemic has brought considerable loss to the world by means of pneumonia related mortality. In the current study, we aimed to discover the predictors of mortality and other worse outcomes in atypical pneumonia cases during the COVID-19 outbreak.

Initial serum lactate levels of COVID-19 patients

Aim: In this study, we aimed to research whether the serum lactate of the COVID-19 patients presented to the initial emergency department can be used to make prognosis of the patients. Materials and Methods: A total of 39 COVID-19 patients were included in the study. Fourteen (35.8%) patients were over 65 years old (Group 1). Twenty-five patients (64.2%) were under 65 years old (Group 2). The diagnosis was made via the oro-nasopharyngeal swab PCR test. We noted the demographic data (age, gender, comorbidities), initial (emergency service presentation) complete blood count parameters including WBC, Plt/Lymp ratio, CRP, procalcitonin and serum lactate levels. We also noted the hospitalization unit (clinic/intensive care unit), hospitalization length, and the outcomes. Patients were divided into two groups according to 65 years and the groups' laboratory results with the prognosis. Results: The mean age of the study group was 55 years. Fourteen (35.8%) patients were over 65 years old. Serum lactate levels did not significantly differ between groups. Hospital length of stay was significantly longer in patients over 65 years of age. Age and hospitalization length were positively correlated with age in all patients. Discussion: Serum lactate level measurement has recently become an important parameter especially for critically ill patients. It is beneficial for predicting the severity and prognosis in severe infections. Serum lactate levels in COVID-19 patients did not differ between age groups. Hospitalization length was longer in elderly patients.