Interprofessional teamwork plays a key role in the uptake of evidence-based interventions, such as noninvasive ventilation (NIV) for patients with exacerbated Chronic Obstructive Pulmonary Disease (COPD). We aimed to identify the shared cognitive tasks in interprofessional teams using NIV for patients with COPD exacerbation. We used a cognitive task analysis approach (CTA) to engage nurses, rapid response team members, respiratory therapists, and physicians involved in the use of NIV to treat patients with COPD exacerbation....

Additional file 1. Table S1. Correlations between CNN and manual gold standard on the artifacts-free images. Table S2. Internal validation: correlations on images with artifacts for the proposed CNN and the commercial software (Circle) in respect to the manual gold standard (GT). Also, the results relevant to interobserver variability between O1 and O2 reported for comparison. Table S3. External validation: correlations between CNN and manual gold standard on images with artifacts. Figure S1. Encoder module....

Interprofessional teamwork plays a key role in the uptake of evidence-based interventions, such as noninvasive ventilation (NIV) for patients with exacerbated Chronic Obstructive Pulmonary Disease (COPD). We aimed to identify the shared cognitive tasks in interprofessional teams using NIV for patients with COPD exacerbation. We used a cognitive task analysis approach (CTA) to engage nurses, rapid response team members, respiratory therapists, and physicians involved in the use of NIV to treat patients with COPD exacerbation....

Abstract Background Segmentation of cardiovascular magnetic resonance (CMR) images is an essential step for evaluating dimensional and functional ventricular parameters as ejection fraction (EF) but may be limited by artifacts, which represent the major challenge to automatically derive clinical information. The aim of this study is to investigate the accuracy of a deep learning (DL) approach for automatic segmentation of cardiac structures from CMR images characterized by magnetic susceptibility artifact in patient with cardiac implanted...

Dentists prescribe 10% of all outpatient antibiotic prescriptions in the United States with infection prophylaxis as the most common reason for prescriptions. Dentists provide antibiotic prophylaxis for prevention of infective endocarditis (IE) and or prosthetic joint infection (PJI). While many studies have identified patterns of dental antibiotic prescribing focusing on antibiotics prescribed, geographic prescribing variations, and provider specialty, one feature not studied is the influence of dental setting. The Departments of Veterans Affairs is the...

Dentists prescribe 10% of all outpatient antibiotic prescriptions in the United States with infection prophylaxis as the most common reason for prescriptions. Dentists provide antibiotic prophylaxis for prevention of infective endocarditis (IE) and or prosthetic joint infection (PJI). While many studies have identified patterns of dental antibiotic prescribing focusing on antibiotics prescribed, geographic prescribing variations, and provider specialty, one feature not studied is the influence of dental setting. The Departments of Veterans Affairs is the...

Additional file 1. Table S1. Correlations between CNN and manual gold standard on the artifacts-free images. Table S2. Internal validation: correlations on images with artifacts for the proposed CNN and the commercial software (Circle) in respect to the manual gold standard (GT). Also, the results relevant to interobserver variability between O1 and O2 reported for comparison. Table S3. External validation: correlations between CNN and manual gold standard on images with artifacts. Figure S1. Encoder module....

Abstract Background Segmentation of cardiovascular magnetic resonance (CMR) images is an essential step for evaluating dimensional and functional ventricular parameters as ejection fraction (EF) but may be limited by artifacts, which represent the major challenge to automatically derive clinical information. The aim of this study is to investigate the accuracy of a deep learning (DL) approach for automatic segmentation of cardiac structures from CMR images characterized by magnetic susceptibility artifact in patient with cardiac implanted...