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- MakaleYalnız künye2024Scopus SJRQ3Efficacy of fat quantification methods used in MRI to distinguish between normal, benign, and malignant bone marrow pathologies in children
Tonkaz, Duygu Erkal; Ozpar, Rifat; Tonkaz, Mehmet; Yazici, Zeynep
Background Fat quantification methods in magnetic resonance imaging (MRI) have been studied to differentiate bone marrow pathologies in adult patients; however, scarce literature is available in pediatric patients.Purpose To evaluate the efficacy of the T1 signal intensity value (T1-SIV), out-of-phase/in-phase signal ratio (OP/IP SR), and fat fraction (FF) to differentiate between normal, benign, and malignant pathological processes.Material and Methods A total of 48 pediatric patients with lumbar and pelvic MRI were classified into three groups according to bone marrow pathology (group 1, normal; group 2, benign pathology/reconversion; group 3, malignant). The efficacy of T1-SIV, OP/IP SR, and FF values in differentiating these pathologies was evaluated using Kruskal-Wallis or analysis of variance and followed by Bonferroni or Dunn-Bonferroni tests. Cutoff values for malignant infiltration were defined using ROC analysis.Results Although these values were significantly different in all three groups (P = 0.001-0.008), this difference was not sufficient to discriminate between all groups. Subgroup analyses showed significant differences in T1-SIV between groups 1-3, in OP/IP SR between groups 1-3, 2-3, and 1-2, in FF between groups 1-2 and 1-3 in various regions (P = 0.001-0.049). Cutoff values had a sensitivity and specificity of 90%-100% for OP/IP SR and FF.Conclusion T1-SIV, OP/IP SR, and FF may potentially distinguish normal from pathological bone marrow. OP/IP SR and FF values detected malignant infiltration with high sensitivity and specificity in this study. However, only OP/IP SR may significantly differentiate benign and malignant bone marrow pathologies which needs to be confirmed in the future study with a larger patient population.
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İncele - MakaleAçık Erişim2024Scopus SJRQ3Prevalence and Determinants of Workplace Violence Against Pediatric Emergency Healthcare Workers and Its Effect on Their Psychological Resilience
Ustuner Top, Fadime; Kulakaç, Nurşen; Cam, Hasan Hüseyin
The study aimed to determine the prevalence and risk factors of workplace violence (WPV) against pediatric emergency healthcare workers and evaluate the relationship between WPV and psychological resilience. This study is cross-sectional and correlational. According to the results, the prevalence of WPV was 69.8%, and its distribution was as follows: verbal abuse (56.9%), bullying/mobbing (37.6%), physical abuse (12.8%), and sexual abuse (2.2%). Being single/separated/divorced/widowed (odds ratio [OR]: 1.85, 95% confidence interval [CI]: 1.03-3.30), being a physician (OR: 4.74, 95% CI: 1.73-12.96), being a staff member (OR: 2.57, 95% CI: 1.10-5.99), routine direct physical contact with patients/clients (OR: 2.77, 95% CI: 1.40-5.48), and lack of encouragement to report WPV (OR: 3.76, 95% CI: 2.01-7.01) were independent predictors of WPV (P <.05), and WPV was found to be associated with low psychological resilience. Arrangements related to violence prevention, preparation, and intervention should be made and maintained in all pediatric emergency departments.
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İncele - BildiriAçık Erişim2021Pneumonia Detection from Pediatric Lung X-Ray Images with Convolutional Neural Network Method
Demir, Yasin; Bingöl, Ozkan
Pneumonia can be cause diseases such as cough, fever and respiratory problems and to death in the progressive situations. Mostly lung x-ray images are used for diagnosis of pneumonia. In this study, an unique convolutional neural network model that can help experts in detection of pneumonia using lung x-ray images below 5 years of age. In the model, open access to 5840 images has been used, and various data increasing methods were used to increase the performance of the training and the success of the method. With the proposed convolutional neural network model, an accuracy rate of 94.55% has been achieved.
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