Universidad Internacional del Ecuador
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A machine learning approach to predict foot care self-management in older adults with diabetes
No full textBackground: Foot care self-management is underutilized in older adults and diabetic foot ulcers are more common in older adults. It is important to identify predictors of foot care self-management in older adults with diabetes in order to identify and support vulnerable groups. This study aimed to identify predictors of foot care self-management in older adults with diabetes using a machine learning approach. Method: This cross-sectional study was conducted between November 2023 and February 2024. The data were collected in the endocrinology and metabolic diseases departments of three hospitals in Turkey. Patient identification form and the Foot Care Scale for Older Diabetics (FCS-OD) were used for data collection. Gradient boosting algorithms were used to predict the variable importance. Three machine learning algorithms were used in the study: XGBoost, LightGBM and Random Forest. The algorithms were used to predict patients with a score below or above the mean FCS-OD score. Results: XGBoost had the best performance (AUC: 0.7469). The common predictors of the models were age (0.0534), gender (0.0038), perceived health status (0.0218), and treatment regimen (0.0027). The XGBoost model, which had the highest AUC value, also identified income level (0.0055) and A1c (0.0020) as predictors of the FCS-OD score. Conclusion: The study identified age, gender, perceived health status, treatment regimen, income level and A1c as predictors of foot care self-management in older adults with diabetes. Attention should be given to improving foot care self-management among this vulnerable group
Fotorefraktif Keratektomi Sonrası Kuru Göz Yönetiminde Yenilikçi Tedavi Yaklaşımları: Trehaloz ve Sodyum Hyaluronatın Karşılaştırmalı Analizi
No full textTARSİM Hayvan Hayat Sigortalarında Teminata Esas Fiyata Etkili Olan Faktörlerin Belirlenmesi
No full textALZHEİMER VE PARKİNSON HASTALIĞINDA UMUT VERİCİ NÖROPROTEKTİF VE TERAPÖTİK BİR AJAN: NOBİLETİN
No full textYüzyılın En Yıkıcı İkili Depreminde Depremzede Olan Türk Yoğun Bakım Hemşirelerinin Bakım Verme Deneyimleri: Nitel Çalışma
No full textFirst record of the genus Zygaenoprocris Hampson, 1900 (Lepidoptera: Zygaenidae) from Türkiye (Turkey)
No full textZygaenoprocris persepolis (Alberti, 1938) is found in eastern Türkiye (Turkey). This is the first record of the genus Zygaenoprocris Hampson, 1900 (Lepidoptera: Zygaenidae) from Türkiye. The larval host-plant of Z. persepolis in Türkiye is Atraphaxis spinosa L.</p
DETERMINING THE MODEL OF TOURISM BUSINESS DISTRICT (TBD) IN COASTAL RESORTS: A CASE STUDY OF TURKEY
No full textCoastal resorts, whose dominant economic activities are those of providing an array of recreational services to tourists, reflect this specialization in their land-use patterns. Therefore, the business districts in coastal resorts have a unique morphology, landscape, and land use. However, the literature reflects that there is limited attention to the tourism business districts (TBDs) that have developed in coastal resorts. Moreover, few empirical studies have been conducted in developing countries, such as Thailand, China, and Turkey, as well as developed ones such as United States, Canada, and Italy. This study discusses the TBDs located in Turkey's coastal resorts in terms of location, form, and function. The findings are presented statistically, and detailed maps are presented to explain the TBDs from a geographical and practical perspective. In this study, ArcGIS 10.5 software has been used to perform spatial analysis of the data. The main findings include that Turkish TBDs have similar characteristics in terms of location, form, and function compared to other coastal resorts worldwide. Therefore, it is possible to say that these similar features constitute a model in terms of land use. In addition, the statistical findings of the study are largely similar to those found in the literature
Correlation of TIRADS scoring in thyroid nodules with preoperative fine needle aspiration biopsy and postoperative specimen pathology
No full textIntroduction: The aim of our study is to determine the value of Thyroid Imaging Reporting and Data Systems (TIRADS) scoring in predicting malignancy in thyroid nodules by examining its relationship with fine needle aspiration biopsy and postoperative histopathological results.Materials and Methods: In this study, patients who underwent surgery after ultrasonographic examination and fine needle aspiration biopsy for thyroid nodules at the General Surgery Clinic of & Ccedil;ukurova University Faculty of Medicine between January 2014 and November 2021 were retrospectively analyzed. The thyroid ultrasonography and fine needle aspiration biopsy of the included patients were performed by a clinician with 15 years of experience. The ultrasonographic features of the nodules were re-evaluated by the same clinician, and the American College of Radiology (ACR) TIRADS score was determined. Fine needle aspiration biopsy results were grouped according to Bethesda criteria. Postoperative histopathological examination results were divided into two groups: benign and malignant. The ACR TIRADS score was compared with fine needle aspiration biopsy and histopathological results. The performance of the ACR TIRADS score in predicting malignancy was determined.Results: 79.8% of the 397 patients were female, and the mean age was 50.9 +/- 12.8 years. The mean diameter of the nodules was 27.4 +/- 15.8 mm. There was a significant, positive, but weak correlation between ACR TIRADS and Bethesda (p < 0.001) (r = 0.33). When the ACR TIRADS score was compared with histopathological results, it was found that the rate of malignancy increased as the TIRADS score increased (p < 0.001). The rates of malignancy diagnosis were 0% for TR1, 13.2% for TR2, 21.7% for TR3, 50.3% for TR4, and 72.4% for TR5. The area under the receiver operating characteristic curve for TIRADS in predicting malignancy was 0.747 (95% CI: 0.699-0.796, p < 0.001). TIRADS can distinguish malignancy with 75% accuracy. The optimal cutoff point was determined as TR4 with 80.3% sensitivity and 60.8% specificity.Conclusion: The ACR TIRADS scoring system is an effective risk classification system for thyroid nodules, providing 75% accuracy in predicting malignancy, with 80.3% sensitivity and 60.8% specificity values