PubMed İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.14627/8

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    Article
    Machine Learning Model for Predicting Multidrug Resistance in Clinical Escherichia Coli Isolates: A Retrospective General Surgery Study
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025) Tolan, H.K.; Aydın, İ.; Tanyildizi-Kökkülünk, H.; Karakuş, M.; Akkaya, Y.; Kaya, O.; Işman, F.K.
    Background/Objectives: Escherichia coli is one of the leading causes of surgical site infections (SSIs) and poses a growing public health concern due to its increasing antimicrobial resistance. High rates of extended-spectrum beta-lactamase (ESBL) production among E. coli strains complicate treatment outcomes and emphasize the need for effective surveillance and control strategies. Methods: A total of 691 E. coli isolates from general surgery clinics (2020–2025) were identified using MALDI-TOF MS. Antibiotic susceptibility data and patient variables were cleaned, encoded, and used to predict resistance using the Random Forest, CatBoost, and Naive Bayes algorithms. SMOTE addressed class imbalance, and model performance was assessed through various validation methods. Results: Among the three machine learning models tested, Random Forest (RF) showed the best performance in predicting antibiotic resistance of E. coli, achieving median accuracy, precision, recall, and F1-scores of 0.90 and AUC values up to 0.99 for key antibiotics. CatBoost performed similarly but was less stable with imbalanced data, while Naive Bayes showed lower accuracy. Feature importance analysis highlighted strong inter-antibiotic resistance links, especially among β-lactams, and some influence of demographic factors. Conclusions: This study highlights the potential of simple, high-performing models using structured clinical data to predict antimicrobial resistance, especially in resource-limited clinical settings. By incorporating machine learning into antimicrobial resistance (AMR) surveillance systems, our goal is to support the advancement of rapid diagnostics and targeted antimicrobial stewardship approaches, which are essential in addressing the growing challenge of multidrug resistance. © 2025 by the authors.
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    Psychometrics of Stanford Presenteeism Scale-Short Form in Turkish
    (Cordus, 2022) Teoman, Ezgi; Seren, Arzu Kader Harmanci
    AIM: Presenteeism means that employees feel obliged to go to work even if there is a real problem that they cannot work.The main purpose is to to adapt the "Stanford Presenteeism Scale-Short Form" into Turkish on Nurses. METHOD: This is a methodological study. The study sample included the nurses working at the medical and surgical clinics of two public hospitals in 2017 in Istanbul. A total of 290 nurses participated in the study. Language, content, construct validities, total item correlation analysis, Kaiser Meyer Olkin, Bartlett tests, confirmatory and explanatory factor analysis (EFA), stability, and Cronbach's alpha reliability analyses were tested. RESULTS: The content validity index of the scale was.92. Two items that have correlation values below.40 were removed from the Turkish form. Cronbach's alpha internal consistency coefficient was.762. The structure of the four-item and single-factor Turkish form was confirmed. CONCLUSION: Stanford Presenteeism Scale-Short Form is a valid and reliable tool for the nurses in Turkey. It is recommended to be used among nurses in different studies. Hospital and nursing care service managers should deal with "presenteeism," since it is becoming a critical health human resource workforce issue. Health care managers may use this tool to evaluate the presenteeism level of their employees.