There are about 173942 clinical studies being (or have been) conducted in United States. The country of the clinical trial is determined by the location of where the clinical research is being studied. Most studies are often held in multiple locations & countries.
Building on limitations of prior research, the investigators proposed to develop the Mindful and Self-Compassionate Care Program (MASC) to help caregivers of persons with Alzheimer Disease and Related Dementias (ADRD) manage stress associated with the general caregiver experience including stress stemming from managing challenging patient behaviors. MASC teaches: (1) mindfulness skills; (2) compassion and self-compassion skills; and (3) behavioral management skills. MASC also provides psychoeducation and group-based training and skill practice to facilitate skill uptake and integration within the caregiver experience and tasks.
Capsulomics has developed a prognostic assay for patients with diagnosed Barrett's esophagus (BE). This study will measure how gastroenterologists make surveillance and treatment management decisions when presented with different clinical and prognostic assay information.
Investigators developed an online educational module (ESTIMATE) to teach Gastroenterology (GI) trainees how to estimate polyp size using a snare. Key components include video instruction and real-time feedback incorporated over a 40-item polyp size assessment test. Trainees from GI fellowship programs will be randomized to one of four groups: control (no video, no feedback), video-only, feedback-only, and video + feedback. Participants will classify polyps into one of three size categories:- diminutive (1-5 mm), small (6-9 mm), and large (≥10 mm). Primary outcome is accuracy of polyp size classification [diminutive (1-5 mm), small (6-9 mm), and large (≥10 mm)]. Secondary outcomes include accuracy of exact polyp size (in mm), cumulative accuracy (to plot learning curves), confidence level of polyp size classification, and directionality of inaccuracy (polyp size overestimation vs underestimation).
This study is an open-label, multi-center study evaluating the clinical utility of Renasight in the diagnosis and management of kidney disease.
This study is a substudy being conducted under one pembrolizumab umbrella master study KEYMAKER-U04. The substudy will consist of 2 parts. Part 1 will evaluate the efficacy and safety of coformulated favezelimab/pembrolizumab plus EV and coformulated vibostolimab/pembrolizumab plus EV relative to pembrolizumab plus EV. There will be no comparison of coformulated favezelimab/pembrolizumab plus EV versus coformulated vibostolimab/pembrolizumab plus EV. If ORR and/or DRR are substantially better on coformulated favezelimab/pembrolizumab plus EV and/or coformulated vibostolimab/pembrolizumab plus EV compared with pembrolizumab plus EV, after evaluation of the totality of data, the sponsor might consider Part 2 (expansion) to further characterize the efficacy and safety of the treatment arms under study.
The objective of this study is to collect Optical Coherence Tomography (OCT) data to construct a reference database for the P200TE.
The objective of the study is to evaluate the safety and tolerability of 4 injections of VAX-24 (at 3 dose levels) compared to PCV15 in infants at 2, 4, 6, and 12-15 months of age, in addition to receiving routine US concomitant vaccines. Stage 1 of the study will comprise 3 dose ascending cohorts. Stage 2 of the study will enroll the remainder of the sample size.
In this study, an artificial intelligence model to detect squamous cell carcinomas (SCC) on photos of recessive dystrophic epidermolysis bullosa (RDEB) skin is developed. The ultimate goal is to integrate this model into an app for patients and physicians, to help detect SCCs in RDEB early. SCCs which rapidly metastasize are the main cause of death in adults with RDEB. The earlier an SCC is recognized, the easier it can be removed and the better the outcome. AI leverages computer science to perform tasks that typically require human intelligence and has recently been used to identify skin cancers based on images. We are currently developing an AI approach for early detection of SCC and distinction of malignancy from chronic wounds and other RDEB skin findings. The aim is to create a web application for patients with RDEB to upload images of their skin and get an output as to SCC present/ no SCC. This will be especially valuable for patients with difficult access to medical expertise and those who are hesitant to allow full skin examination at each visit, often because of fear of biopsies. Thus, this project will directly benefit patients by allowing early recognition of SCCs and will empower patients and their families by providing a home use tool. So far, the study team has mainly used professional images (photographs taken in hospital settings by physicians, nurses, and clinical photographers) of both SCCs in RDEB and images of RDEB skin without SCC to develop and train the AI model. The images that are expected in a real-life setting will mostly be pictures taken by patients or family members with their phones or digital cameras. These images have different properties regarding resolution, focus, lighting, and backgrounds. Incorporating such images will be crucial in the upcoming phases of model development-testing and validation-for the web application be a success for patients.
The primary objective is to determine whether continuous sensing, control and mitigation of home indoor air quality influences the frequency of asthma related symptoms, as measured by Serum IgE, Spirometry with exhaled Nitric Oxide, missed school and workdays, need for pharmacologic intervention (albuterol, oral steroids), frequency of sick visits to pulmonologist or primary care provider (PCP), urgent care / emergency department visits, and hospitalizations
The transition to college is a period of elevated risk for a range of mental health conditions. For students with pre-existing mental health diagnoses, the added pressures can exacerbate challenges. Although colleges and universities strive to provide mental health support to their students, the high demand for these services makes it difficult to provide scalable cost-effective solutions. To address these issues, the present study aims to compare the efficacy of three different treatments using a large cohort of 600 students transitioning to college. Interventions were selected based on their potential for generalizability and cost-effectiveness on college campuses. The randomized controlled trial will assign 150 participants to one of four arms: 1) group-based therapy, 2) physical activity program, 3) nature experiences group, or 4) self-monitoring condition as the control group. In addition, biometric data will be collected from all participants using a wearable device to develop algorithmic predictions of mental and physical health functioning. Once recruitment is complete, modeling strategies will be used to evaluate the outcomes and effectiveness of each intervention. The findings of this study will provide evidence as to the benefits of implementing scalable and proactive interventions using technology with the goal of improving well-being and success of new college students.