3 research outputs found

    sj-docx-1-dst-10.1177_19322968231223726 – Supplemental material for Machine Learning Models for Prediction of Diabetic Microvascular Complications

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    Supplemental material, sj-docx-1-dst-10.1177_19322968231223726 for Machine Learning Models for Prediction of Diabetic Microvascular Complications by Sarah Kanbour, Catharine Harris, Benjamin Lalani, Risa M. Wolf, Hugo Fitipaldi, Maria F. Gomez and Nestoras Mathioudakis in Journal of Diabetes Science and Technology</p

    <b>Understanding Providers’ Readiness and Attitudes Toward Autoantibody Screening: A Mixed-Methods Study</b>

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    Screening for autoantibodies associated with type 1 diabetes can identify people most at risk for progressing to clinical type 1 diabetes and can provide an opportunity for early intervention. Drawbacks and barriers to screening exist, and concerns arise, as methods for disease prevention are limited and no cure exists today. The availability of novel treatment options such as teplizumab to delay progression to clinical type 1 diabetes in high-risk individuals has led to the reassessment of screening programs. This study explored awareness, readiness, and attitudes of endocrinology providers toward type 1 diabetes autoantibody screening.</p
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