Publications by authors named "H H Miller"
Non-cadaveric spine surgery simulator training in neurosurgical residency.
N Am Spine Soc J
December 2024
Background: Spine surgical training faces increasing challenges due to restricted working hours and greater sub specialization. Modern simulators offer a promising approach to teaching both simple and complex spinal procedures. This study evaluated the acceptance and efficacy of spine simulator training using a lumbar herniated disc model tested by 16 neurosurgical residents (PGY-1-6), and compared 3D and 2D teaching methods.
View Article and Find Full Text PDFBackground: Dietary Approaches to Stop Hypertension (DASH) is a recommended first-line treatment for adults with hypertension, yet adherence to DASH is low.
Purpose: To evaluate the efficacy of a digital health intervention (DHI), compared with attention control, on changes in DASH adherence and blood pressure among adults with hypertension.
Methods: Nourish was a 12-month, parallel, 2-arm, randomized controlled trial of a virtually delivered DHI.
Cell metabolism is crucial for orchestrating the differentiation and function of regulatory T cells (Tregs). However, the underlying mechanism that coordinates cell metabolism to regulate Treg activity is not completely understood. As a pivotal molecule in lipid metabolism, the role of SHIP-1 in Tregs remains unknown.
View Article and Find Full Text PDFBackground: Several adult studies show mixed reports in clinical outcomes between cryopreserved and fresh stem cell products, with majority reporting no significant differences and others report that there are differences in outcomes. There is limited literature reporting its impact on outcomes in pediatric hematopoietic cell transplantation (HSCT).
Objective: To compare clinical outcomes between fresh vs cryopreserved stem cell treatment in pediatric HSCT.
Article Synopsis
- Early identification of risk factors for prolonged mechanical ventilation (PMV) can lead to timely clinical interventions and reduce complications like infections, especially in the context of COVID-19.
- This study utilized ensemble machine learning (ML) to analyze clinical data at the time of intubation to distinguish between patients at high risk for PMV (more than 14 days) and those not at risk (14 days or less).
- The ML approach demonstrated strong predictive performance, highlighting key clinical markers like glucose levels and platelet counts that can inform patient management and optimize hospital resource allocation.