Plos One
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Observational Study
Evaluation of antibiotic dispensing practice in community pharmacies in Jordan: A cross sectional study.
It is well known that the emergence of antibiotic resistance is linked to the misuse and overuse of antibiotics. Misuse includes self-medication and the inappropriate use of antibiotics because of improper dosage or improper duration than recommended. This study investigated three patterns of dispensing antibiotics in a sample of community pharmacies in Jordan. ⋯ In conclusion, a significant proportion of antibiotics are dispensed without prescription in Jordan. Moreover, a considerable proportion of prescribed antibiotics were inappropriate for the conditions concerned. This indicates the importance of enforcing the Jordanian regulations prohibiting the dispensing of nonprescription antibiotics and the implementation of continuous education to physicians and pharmacists to increase awareness about the emergence of antibiotic resistance.
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Detection of pulmonary nodules is an important aspect of an automatic detection system. Incomputer-aided diagnosis (CAD) systems, the ability to detect pulmonary nodules is highly important, which plays an important role in the diagnosis and early treatment of lung cancer. Currently, the detection of pulmonary nodules depends mainly on doctor experience, which varies. This paper aims to address the challenge of pulmonary nodule detection more effectively. ⋯ Our team trained A-CNN using the LUNA16 and Ali Tianchi datasets and evaluated its performance using the LUNA16 dataset. We discarded nodules less than 5mm in diameter. When the average number of false positives per scan was 0.125 and 0.25, the sensitivity of A-CNN reached as high as 81.7% and 85.1%, respectively.
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Degenerative cervical myelopathy (DCM) is a spinal cord condition that results in progressive non-traumatic compression of the cervical spinal cord. Spine surgeons must consider a large quantity of information relating to disease presentation, imaging features, and patient characteristics to determine if a patient will benefit from surgery for DCM. We applied a supervised machine learning approach to develop a classification model to predict individual patient outcome after surgery for DCM. ⋯ Worse pre-operative disease severity, longer duration of DCM symptoms, older age, higher body weight, and current smoking status were associated with worse surgical outcomes. We developed a model that predicted positive surgical outcome for DCM with good accuracy at the individual patient level on an independent testing cohort. Our analysis demonstrates the applicability of machine-learning to predictive modeling in spine surgery.
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Observational Study
Combining patient visual timelines with deep learning to predict mortality.
Deep learning algorithms have achieved human-equivalent performance in image recognition. However, the majority of clinical data within electronic health records is inherently in a non-image format. Therefore, creating visual representations of clinical data could facilitate using cutting-edge deep learning models for predicting outcomes such as in-hospital mortality, while enabling clinician interpretability. The objective of this study was to develop a framework that first transforms longitudinal patient data into visual timelines and then utilizes deep learning to predict in-hospital mortality. ⋯ We converted longitudinal patient data into visual timelines and applied a deep neural network for predicting in-hospital mortality more accurately than current standard clinical models, while allowing for interpretation. Our framework holds promise for predicting several important outcomes in clinical medicine.
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Workplace bullying experienced by clinical nurses is associated with burnout, a factor that threatens the quality of nursing care and patient safety. This study examined the association of workplace bullying with burnout, professional quality of life, and turnover intention among clinical nurses. A descriptive cross-sectional study was conducted using a structured questionnaire. ⋯ Controlling for the general characteristics of the participants, workplace bullying had a significant association with emotional exhaustion (B = 0.29, p < 0.01) and depersonalization (B = 0.15, p < 0.01) among the subdomains of burnout, compassion fatigue among the components of professional quality of life (B = 0.15, p < 0.01), and turnover intention (B = 0.05, p < 0.01). Thus, preventing workplace bullying is important to reduce clinical nurses' burnout and turnover. The role of nursing leadership is crucial to develop interventions that reduce workplace bullying and successfully create a professional, nurturing, and supportive work culture.