Publications by authors named "M K Underwood"

Background And Objectives: Neonatal encephalopathy (NE) is characterized by an abnormal level of consciousness with or without seizures in the neonatal period. It affects 1-6/1,000 live term newborns. We applied genome sequencing (GS) in term newborns with NE to investigate the underlying genetic causes.

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Purpose: The AVeNEW Post-Approval Study (AVeNEW PAS) follows upon results from the AVeNEW IDE clinical trial and was designed to provide additional clinical evidence of safety and effectiveness using the Covera™ Vascular Covered Stent to treat arteriovenous fistula (AVF) stenoses in a real-world hemodialysis patient population.

Materials And Methods: One hundred AVF patients were prospectively enrolled at 11 clinical trial sites in the USA and treated with the covered stent after angioplasty of a clinically significant target stenosis. The primary safety outcome was freedom from any adverse event that suggests the involvement of the AV access circuit evaluated at 30 days.

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Introduction: Sciatica is a debilitating condition that often becomes chronic, and for which there are few effective treatment options. Treatments such as the anti-depressant duloxetine have shown promise, but the evidence is inconclusive. We are describing a high quality, definitive trial to investigate the efficacy, safety and cost-effectiveness of duloxetine in chronic sciatica.

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Introduction: Surgical patients frequently experience post-operative complications at home. Digital remote monitoring of surgical wounds via image-based systems has emerged as a promising solution for early detection and intervention. However, the increased clinician workload from reviewing patient-submitted images presents a challenge.

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Article Synopsis
  • The study compares machine learning approaches to traditional statistical models for survival analysis, specifically in predicting time to return to work for families with complex issues.
  • The results show that no model clearly outperformed the others, with both machine learning and classical models displaying low predictive power.
  • While machine learning approaches exhibited better fit metrics, particularly the Random Survival Forest, this did not lead to significantly improved predictive accuracy over classical methods, indicating the need for further refinement of these algorithms.
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