2023 CiteScore: 0.8
pISSN: 2383-4196
eISSN: 2383-420X
Editor-in-Chief:
Hojjat Zeraati, PhD.
Vol 10 No 4 (2024): .
Articles
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Background: Self-esteem is one of the key foundations of human personality and is known as an important component in mental health and social psychology. Paying attention to the factors that contribute to students' academic achievement is a step toward long-term development and leads to proper planning to advance educational goals. This study was devoted to cluster undergraduate students of Shahrekord University of Medical Sciences based on their self-esteem and academic achievement.
Methods: The multi-stage cluster sampling method was used to select 260 undergraduate students from various fields in 2022. The data collection tool included a background information checklist, a 10-item self-esteem questionnaire, and a 39-item academic achievement questionnaire. The elbow method was used to estimate the number of optimal clusters. The NbClust package in R 4.2.1 software was used for clustering analysis based on the k-means approach.
Results: In this study, out of 260 participating students, 176 (67.7%) were girls and 84 (32.3%) were boys. The overall average ± standard deviation of academic achievement was 105.2 ± 10.3. There is a positive and significant correlation (r = 0.44) between academic achievement and self-esteem (P-value <0.001). The optimal number of clusters was estimated as four based on the elbow method. Self-esteem in cluster number 1 with 35 students was at the lowest level at -2.6±2.9. Cluster number 4 with 48 students had less academic progress with 94.0 ± 6.1 than the other three clusters.
Conclusion: Promoting the self-esteem and academic achievement of students is the duty of students, professors and colleges.
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Context. The term "school pathologies" encompasses two distinct categories of health disorders: those that are caused or exacerbated by a lack of physical activity, and those that predominantly affect children of school age. The objective of this epidemiological study is to ascertain the physical capacity of students with pathologies to engage in physical education and sports (PES) classes. Methods. The methodology employed in this study is based on an analysis of 93,870 medical records. This study is based on a survey conducted in the Beni Mellal-Khénifra region, which included 69 public secondary schools. The data were derived from 93,870 medical records of students aged 13 to 18. These records were analyzed to identify 918 confirmed pathological cases, with a detailed breakdown by gender and urban/rural area. The measurement instrument included the use of international references for the classification of health anomalies, followed by statistical analysis of the data using SPSS version 27 software. Results. The results indicate a notable variation in the prevalence of different diagnosed and confirmed school pathologies, with no discernible difference between the sexes. The majority of cases were found in urban areas (64.38%), and the most dominant age group was 16-18 (59.59%; p < .05). A prevalence of 40.20% of pupils, or 3.93‰ of the diagnosed population, are totally exempt from physical practice in PES, varying according to the types and characteristics of the pathology. Three out of every 1,000 children are unable to participate in physical education at school due to medical restrictions. This phenomenon is not influenced by sex, but varies according to area and age. This indicates a trend of association between urban area and age for physical inactivity. Conclusion. This work underscores the necessity of measuring the physical activity of these students, whether at school or elsewhere, in order to gain a more comprehensive understanding of their physical, social, and mental well-being, as well as their experience of physical education.
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Introduction: According to the significance of children in the culture, economy, and human resources for the country's future, their growth in the first 2 years and its influencing factors are crucial for the country's progress.
Objectives: The study evaluates the determinants that impact the growth of children in their first two years of life.
Methods: This data was collected based on electronic files from 601 children who visited Health Service Centers in North Khorasan province from April 2020 to April 2023. We used longitudinal models (transmission-random-marginal) and SPSS software version 26. We employed the Akaike information criterion, with a significance level of 0.05.
Results: Based on our research, we found that the recommended growth measurements for weight, length, and head circumference for children under 2 years using the longitudinal method. Children's growth was significantly associated with gender, place of residence, mother's education, type of breastfeeding, intrauterine age, singleton birth, and mother's weight (P<0.05). Boys showed better weight growth compared to girls, but the rate of weight and height growth was similar for both genders.
Conclusions: According to the results of this study, adequate training on factors affecting children's growth should be provided to both mothers and health workers, which may reduce the risk of developing disorders.
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Abstract
Background:
The hidden assumption in most of survival analysis models is the occurrence of the event of interest for all study units. The violation of this assumption occurs in several situations. For example, in medicine, some patients may never have cancer, and some may never face Alzheimer. Ignoring such information and analyzing the data with traditional survival models may lead to misleading results. Analyzing long term survivals can be performed using both traditional and neural networks. There has been an increasing interest in modeling lifetime data using neural network. However, for long-term survivors only one neural network was introduced to estimate the uncured proportion together with the EM algorithm to account for the latency part. Neural network in survival analysis requires special cost function to account for censoring.
Methods: In this paper, we extend the neural network using pseudo values to analyze cure fraction model. It neither requires the use of special cost function nor the EM algorithm.
Results: The network is applied on both synthetic and Melanoma real datasets to evaluate its performance. We compared the results using goodness of fit methods in both datasets with cox proportional model using EM algorithm.
Conclusion: The proposed neural network has the flexibility of analyzing data without parametric assumption or special cost function. Also, it has the advantage of analyzing the data without the need of EM algorithm. Comparing the results with cox proportional model using EM algorithm, the proposed neural network performed better.
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Introduction: This study examines the significance of developing a predictive approach for assessing the prognosis of individuals diagnosed with COVID-19. This method can help physicians make treatment decisions that decrease mortality and prevent unnecessary treatments. This study also emphasizes the significance of radiomics features. Therefore, our objective was to assess the predictive capabilities of Computed Tomography-based radiomics models using a dataset comprising 577 individuals diagnosed with COVID-19.
Methods: The U-net model was applied to automatically perform whole lung segmentations, extracting 107 texture, intensity, and morphological features. We utilized two feature selectors and three classifiers. We assessed the random forest, logistic regression, and support vector machines by implementing a five-fold cross-validation approach. The precision, sensitivity, specificity, accuracy, F1-score, and area under the receiver operating characteristic curve were reported.
Result: The random forest model achieved an area under the receiver operating characteristic curve, precision, sensitivity, specificity, accuracy, and F1-score in the range of 0.85 (CI95%: 0.76–0.91), 0.75, 0.82, 0.78, 0.68, and 0.71, respectively. Logistic regression attained an area under the receiver operating characteristic curve of 0.80 (CI95%: 0.72–0.88), corresponding to values of 0.88, 0.62, 0.74, 0.55, and 0.67, respectively. Support Vector Machines computes the above six metrics as an area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, precision, and F1-score in the range of 0.69 (CI95%: 0.59–0.79), 0.68, 0.64, 0.66, 0.5, and 0.57, respectively.
Conclusion: We are developing a robust radiomics classifier that accurately predicts mortality in COVID-19 patients. Lung Computed Tomography radiomics features may aid in identifying high-risk individuals who need supplementary therapy and decrease the propagation of the virus.
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Background: Diabetes mellitus is one of the highest causes of death around the world as one out of eleventh adults have diabetes mellitus. In Jordan, the prevalence of diabetes mellitus was projected to be around 16% in 2020. Our study aims to understand the compliance and efficacy for self-management among refugees living with diabetes mellitus in the Jordanian Nuzha health centers.
Methods: Structured interviews with short questionnaires, focus group discussions (FGDs), and semi-structured interviews with healthcare providers. The study population was based on a sample of patients who visited the Nuzha health centers.
Results: A total of 30 participants at UNRWA Nuzha Health Center participated in the questionnaire. Notably, most participants demonstrated high self-efficacy for controlling one’s DM (83%) and high perceived ability to find the support and medical resources for management (87%). Additionally, most participants showed robust knowledge in the importance of diet and exercise for the management of DM (93% for both variables). This study also reports that 11 participants were overweight, 9 had Class I obesity and 6 had Class II obesity.
Conclusions: Limitations of this study included a low number of female patients during FGDs, limited number of Type I DM patients, and limited ages. Our main findings are that patients of Nuzha HC have high perceived self-efficacy and structural support for managing DM, level of education impacts management of diabetes, transportation is a major barrier to receiving consistent care and healthy dietary options are not affordable
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Introduction: During the outbreak of COVID-19, most hospitals faced resource shortages due to the great surges in the influx of infected COVID-19 patients and demand exceeding capacities. Predicting the lengths of stay (LOS) of the patients can help to make proper resource-planning decisions. CT-SS accurately determines the disease severity and could be considered an appropriate prognostic factor to predict patients’ LOS.
Objective: In this study, we evaluate the additive value of CT-SS in the prediction of hospital and ICU LOSs of COVID-19 patients.
Methods: This single-center study retrospectively reviewed a hospital-based COVID-19 registry database from 6854 cases of suspected COVID-19. Four well-known ML classification models including kNN, MLP, SVM, and C4.5 decision tree algorithms were used to predict hospital and ICU LOSs of COVID-19 patients. The confusion matrix-based performance measures were used to evaluate the classification performances of the ML algorithms.
Results: For predicting hospital LOS, the MLP model with an accuracy of 96.7%, sensitivity of 100.0%, precision of 93.8%, specificity of 93.4%, and AUC of around 99.4% had the best performance among the other three ML techniques. This algorithm with 95.3% sensitivity, 86.2% specificity, 90.8% accuracy, 87.3% precision, 91.2% F-Measure, and an AUC of 95.8% had also the best performance for predicting ICU LOS of the patients.
Conclusion: The performances of the ML predictive models for predicting hospital and ICU LOSs of COVID-19 patients were improved when CT-SS data was integrated into the input dataset.
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Background: Granulomatosis with polyangiitis (GPA), previously known as Wegener's granulomatosis is a systemic, necrotizing vasculitis. To our knowledge, there have been no previous attempts to assess the literature on GPA through scientometric analysis. In our study, we utilized scientometric analysis to explore the geographical, institutional, publication, authorship, citation, and keyword dimensions of GPA research, with the goal of uncovering the current state and emerging trends in the field.
Method: The bibliographic information for studies on GPA was obtained from Scopus up to 2024. VOSviewer software was used to analyze publication characteristics, including countries, institutions, journals, authors, core references, and keywords.
Results: The literature review yielded 15092 publications in the Title-abstract-keyword fields related to GPA. The number of published articles increased from 2014 to 2021, and decreased since 2021. The United States (n=3672, 24.3%), has the highest publication number. There was a strong and significant positive correlation between the number of articles produced by countries on GPA and their gross domestic product (GDP) (r = 0.7103, P < 0.001). Mayo Clinic (n=353) is the most active institution and the Journal Of Rheumatology (n=248) is the most active journal. The analysis of the co-occurrences of keywords was performed by VOSviewer. The most frequent author keyword was “Wegener’s Granulomatosis” (n=1718).
Conclusion: The current study comprehensively reviewed GPA research from 1970 to 2024 using Scopus-indexed articles. Results highlighted leading countries, institutions, journals, influential publications, and key authors, identifying impactful research avenues. This scientometric review offers valuable insights for future research directions and publishing strategies in GPA. By recognizing trends and emerging themes, clinicians can enhance their practice, engage in relevant research, and contribute to improved patient outcomes.
