The United States Centers for Disease Control and Prevention (CDC), through U.S. President's Emergency Plan for AIDS Relief (PEPFAR), supports a third of all people receiving HIV care globally. CDC works with local partners to improve methods to find, treat, and prevent HIV and tuberculosis. However, a shortage of trained medical professionals has impeded efforts to control the HIV epidemic in Sub-Saharan Africa and Asia. The Project Extension for Community Healthcare Outcomes (ECHO) model expands capacity to manage complex diseases, share knowledge, disseminate best practices, and build communities of practice. This manuscript describes a practical protocol for an evaluation framework and toolkit to assess ECHO implementation. This mixed methods, developmental evaluation design uses an appreciative inquiry approach, and includes a survey, focus group discussion, semi-structured key informant interviews, and readiness assessments. In addition, ECHO session content will be objectively reviewed for accuracy, content validity, delivery, appropriateness, and consistency with current guidelines. Finally, we offer a mechanism to triangulate data sources to assess acceptability and feasibility of the evaluation framework and compendium of monitoring and evaluation tools. This protocol offers a unique approach to engage diverse group of stakeholders using an appreciative inquiry process to co-create a comprehensive evaluation framework and a compendium of assessment tools. This evaluation framework utilizes mixed methods (quantitative and qualitative data collection tools), was pilot tested in Tanzania, and has the potential for contextualized use in other countries who plan to evaluate their Project ECHO implementation.
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http://dx.doi.org/10.3389/fpubh.2021.714081 | DOI Listing |
Med Biol Eng Comput
January 2025
Department of Computer Science and Engineering, Shri Shankaracharya Institute of Professional Management and Technology, Raipur, (C.G.), India.
This study presents an advanced methodology for 3D heart reconstruction using a combination of deep learning models and computational techniques, addressing critical challenges in cardiac modeling and segmentation. A multi-dataset approach was employed, including data from the UK Biobank, MICCAI Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, and clinical datasets of congenital heart disease. Preprocessing steps involved segmentation, intensity normalization, and mesh generation, while the reconstruction was performed using a blend of statistical shape modeling (SSM), graph convolutional networks (GCNs), and progressive GANs.
View Article and Find Full Text PDFLangenbecks Arch Surg
January 2025
Department of Urology, Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
Background: There are multiple surgical approaches for treating symptomatic simple renal cysts (SSRCs). The natural orifice transluminal endoscopic surgery (NOTES) approach has gradually been applied as an emerging minimally invasive approach for the treatment of SSRCs. However, there are no clear indicators for selecting the NOTES approach for patients with SSRCs.
View Article and Find Full Text PDFRandomized controlled trials (RCTs) evaluating anti-cancer agents often lack generalizability to real-world oncology patients. Although restrictive eligibility criteria contribute to this issue, the role of selection bias related to prognostic risk remains unclear. In this study, we developed TrialTranslator, a framework designed to systematically evaluate the generalizability of RCTs for oncology therapies.
View Article and Find Full Text PDFCommun Med (Lond)
January 2025
International Research Center for Neurointelligence, The University of Tokyo, Tokyo, Japan.
Background: In-person interaction offers invaluable benefits to people. To guarantee safe in-person activities during a COVID-19 outbreak, effective identification of infectious individuals is essential. In this study, we aim to analyze the impact of screening with antigen tests in schools and workplaces on identifying COVID-19 infections.
View Article and Find Full Text PDFPurpose Of Review: This review aims to evaluate the impact of artificial intelligence (AI) on cancer health equity, specifically investigating whether AI is addressing or widening disparities in cancer outcomes.
Recent Findings: Recent studies demonstrate significant advancements in AI, such as deep learning for cancer diagnosis and predictive analytics for personalized treatment, showing potential for improved precision in care. However, concerns persist about the performance of AI tools across diverse populations due to biased training data.
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