Publications by authors named "Jens Magelund Tarp"

Purpose: Adult growth hormone deficiency (AGHD) is often underdiagnosed and undertreated, leading to costly comorbidities. Previously, we developed an algorithm to identify individuals in a commercially insured US population with high, moderate, or low likelihood of having AGHD. Here, we estimate and compare direct medical costs by likelihood level.

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Introduction: Increasing interest in real-world evidence has fueled the development of study designs incorporating real-world data (RWD). Using the , we specify three designs to evaluate the difference in risk of major adverse cardiovascular events (MACE) with oral semaglutide versus standard-of-care: (1) the actual sequence of non-inferiority and superiority randomized controlled trials (RCTs), (2) a single RCT, and (3) a hybrid randomized-external data study.

Methods: The hybrid design considers integration of the PIONEER 6 RCT with RWD controls using the experiment-selector cross-validated targeted maximum likelihood estimator.

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Time spent in glycemic target range (time in range [TIR]; plasma glucose 70-180 mg/dL [3.9-10.0 mmol/L]) as a surrogate endpoint for long-term diabetes-related outcomes requires validation.

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Continuous glucose monitoring (CGM) has revolutionized the world of diabetes and transformed the approach to diabetes care. In this context, an expert panel has reached consensus on clinical targets for CGM data interpretation based on eight CGM metrics. At least 70% of 14 consecutive CGM days (referred to as a period) are recommended to assess glycemic control based on the metrics.

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Two of the classical hallmarks of cancer are uncontrolled cell division and tissue invasion, which turn the disease into a systemic, life-threatening condition. Although both processes are studied, a clear correlation between cell division and motility of cancer cells has not been described previously. Here, we experimentally characterize the dynamics of invasive and non-invasive breast cancer tissues using human and murine model systems.

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