To develop a radiomics score using ultrasound images to predict thyroid malignancy and to investigate its potential as a complementary tool to improve the performance of risk stratification systems. We retrospectively included consecutive patients who underwent fine-needle aspiration (FNA) for thyroid nodules that were cytopathologically diagnosed as benign or malignant. Nodules were randomly assigned to a training and test set (82 ratio). A radiomics score was developed from the training set, and cutoff values based on the maximum Youden index (Rad_maxY) and for 5%, 10%, and 20% predicted malignancy risk (Rad_5%, Rad_10%, Rad_20%, respectively) were applied to the test set. this website The performances of the American College of Radiology (ACR) and the American Thyroid Association (ATA) guidelines were compared with the combined performances of the guidelines and radiomics score with interpretations from expert and nonexpert readers. A total of 1624 thyroid nodules from 1609 patients (mean age, 50.1 years [range, ules in combination with risk stratification systems, by improving specificity, accuracy, and PPV and unnecessary FNA rates while maintaining sensitivity for both expert and nonexpert readers.• The radiomics score yielded an AUC of 0.85 and 0.75 in the training and test set, respectively. • For all readers, combining a 5% predicted malignancy risk cutoff for the radiomics score with the ACR and ATA guidelines significantly increased specificity, accuracy, and PPV and decreased unnecessary FNA rates, with no decrease in sensitivity. • Radiomics can help predict malignancy in thyroid nodules in combination with risk stratification systems, by improving specificity, accuracy, and PPV and unnecessary FNA rates while maintaining sensitivity for both expert and nonexpert readers. To compare diagnosis performance and effective dose of ultra-low-dose CT (ULD CT) versus radiographs in suspected spinal or pelvic ring or hip fracture for minor trauma. ULD CT, in addition to radiography, was prospectively performed in consecutive patients admitted to the emergency department for minor traumas, during working hours over 2 months. Presence of a recent fracture was assessed by two blind radiologists independently. Sensitivities and specificities were estimated using the best valuable comparator (BVC) as a reference and using a latent class model in Bayesian inference (BLCM). Dosimetric indicators were recorded and effective doses (E) were calculated using conversion coefficient. Eighty areas were analyzed in 69 patients, including 22 dorsal spine, 28 lumbar spine, and 30 pelvic ring/hip. Thirty-six fractures (45%) were observed. Applying the BVC method, depending on location, ULD CT sensitivity was 80 to 100% for reader 1 and 85 to 100% for reader 2, whereas radiographic sensitivity was ure detection when compared with radiographs. • The effective dose of ultra-low-dose spine and pelvis CT scan and radiographs is comparable. • Replacement of radiographs by ULD CT in daily practice for trauma patients is an option to consider and should be evaluated by a randomized trial.Big Data technology is one of the most promising organizational processes within the Healthcare and Pharmaceutical industry and crucial for any company that wants to preserve the competitive advantage in the market, where most of the organizational structures are already struggling with the right skills and knowledge to fully support existing business needs for storing and processing and even analyzing information. This paper aims to examine the extent to which new Big Data technology and data-related processes are developing different professionals skills and competencies within the Healthcare and Pharmaceutical industries, and creating sustainable development in addressing critical organizational challenges in recruiting, retaining, and discover professional skills that can fully support the advances and exponential growth of Big Data technology benefits. This research paper also highlights the significant aspects of Big Data in professional technical and process oriented skills development, and the influenmple of companies, organizations, and interviewees. Studying a broader set of similar research questions in more homogeneous organizations could provide deeper insights into the process, governance, and stakeholder dimensions of Big Data within specific contexts. Therefore this study contributes to explore in-depth and systematically to what extent Big Data technology and processes are currently influencing the healthcare and pharmaceuticals industries where to the best of the authors' knowledge, it is the first focus group dealing with the presented research questions. Hyperparathyroidism persists in up to 50% of pediatric kidney transplant recipients. The aims of this study were to describe the evolution of parathyroid hormone (PTH) in the first year after transplantation and to identify factors associated with hyperparathyroidism. This retrospective study included children who underwent kidney transplantation at the University Hospitals of Ghent, Leuven, Rotterdam, or Amsterdam. Data from 149 patients were collected before and up to 12 months after transplantation. Severe hyperparathyroidism was defined as PTH 2-fold above the reference value. Factors associated with hyperparathyroidism and severe hyperparathyroidism were identified using multivariate logistic regression analysis. Before transplantation, 97 out of 137 patients (71%) had hyperparathyroidism. The probability of hyperparathyroidism and severe hyperparathyroidism declined from 0.49 and 0.17 to 0.29 and 0.09 at 3 and 12 months after transplantation, respectively. BMI SDS (β 0.509; p = 0.011; 95% CI 1.122-2.468), eGFR (β - 0.227; p = 0.030; 95% CI 0.649-0.978), and pre-transplant hyperparathyroidism (β 1.149; p = 0.039; 95% CI 1.062-9.369) were associated with hyperparathyroidism 12 months after transplantation. Pre-transplant hyperparathyroidism (β 2.115; p = 0.044; 95% CI 1.055-65.084), defined as intact parathormone (iPTH) levels > 65 ng/l (6.9 pmol/l) or 1-84 PTH > 58 ng/l (6.2 pmol/l), was associated with severe hyperparathyroidism at 3 months. Only eGFR (β - 0.488; p = 0.010; 95% CI 0.425-0.888) was inversely associated with severe hyperparathyroidism at 9 months after transplantation. Allograft function remains the main determinant of severe hyperparathyroidism after transplantation. Our findings emphasize the importance of BMI and pre-transplant PTH control.Allograft function remains the main determinant of severe hyperparathyroidism after transplantation. Our findings emphasize the importance of BMI and pre-transplant PTH control.