In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.

In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.

In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.

In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.

In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.

In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.

This study is for subjects who has been diagnosed with radioactive iodine refractory (RAIR) differentiated thyroid cancer. Subjects are expected to remain in the study for a minimum of 96 months. Drugs are FDA approved and is given in the form of Tablet to subjects. The procedures include urine protein test, CT, MRI. Risks include diarrhea, nausea, vomiting, tiredness, weight loss, loss of appetite, changes in taste, redness, pain or peeling of palms and soles, High blood pressure. There is evidence that dabrafenib, trametinib and cabozantinib are effective in stabilizing and shrinking the type of cancer, we do not know which of these approaches are better at prolonging time until tumor growth. However, information learned from the trial may help other people in the future.

This study is for subjects that have been diagnosed with low-grade upper tract urothelial cancer (LG-UTUC). This study is to evaluate the tumor ablative effect of the study drug (UGN-104). Subject are expected to reman in the study for a minimum of 15months or longer.

The purpose of this research study is to evaluate patient outcomes following treatment with the SPRINT® Peripheral Nerve Stimulation (PNS) System for chronic posterior sacroiliac joint complex (PSIJC) pain. The PNS system is a temporary nerve stimulator, which provides non-surgical treatment that reduces pain by sending electrical pulses to the nerves that carry signals to/from your spinal cord. SPRINT PNS system is currently FDA approved to treat pain after surgery, pain after trauma, and pain that is difficult to treat. The study will consist of 3 total visits for each participant, over the course of roughly 6 months.

This study is for people who have experienced a traumatic event in the past one year and drink alcohol. The research involves completing a five week behavioral treatment for stress and alcohol use. Participants will complete surveys during visits. Participants may also be asked to complete a interview about their experiences.