Treasure (Hunt for) Your Health! Addressing Pediatric Social Determinants of Health Through Child-Friendly Community Engagement Events.

Social determinants of health (SDoH), including factors such as education level, housing, poverty, racism, and food insecurity and their impact on health outcomes have been well documented. The "Wayne Pediatrics Health and Nutrition Expo" held at Detroit's Eastern Market was an activity-based health and nutrition event addressing pediatric SDoH. Partnering with community organizations, the event had 10 stations addressing SDoH: access to a primary-care pediatrician; HIV-care and prevention; childhood literacy; clothing & winter coats; mental health and childhood development; nutrition; staying active; vaccination; and food insecurity.

T1-Weighted, Dynamic Contrast-Enhanced MR Perfusion Imaging Can Differentiate between Treatment Success and Failure in Spine Metastases Undergoing Radiation Therapy.

Background And Purpose: Current imaging techniques have difficulty differentiating treatment success and failure in spinal metastases undergoing radiation therapy. This study investigated the correlation between changes in dynamic contrast-enhanced MR imaging perfusion parameters and clinical outcomes following radiation therapy for spinal metastases. We hypothesized that perfusion parameters will outperform traditional size measurements in discriminating treatment success and failure.

Influence of socioeconomic factors on discharge disposition following traumatic cervicothoracic spinal cord injury at level I and II trauma centers in the United States.

Background: Discharge to acute rehabilitation is strongly correlated with functional recovery after traumatic injury, including spinal cord injury (SCI). However, services such as acute care rehabilitation and Skilled Nursing Facilities (SNF) are expensive. Our objective was to understand if high-cost, resource-intensive post-discharge rehabilitation or alternative care facilities are utilized at disparate rates across socioeconomic groups after SCI.

A Computational Platform Integrating a Mechanistic Model of Crohn's Disease for Predicting Temporal Progression of Mucosal Damage and Healing.

Introduction: Physicians are often required to make treatment decisions for patients with Crohn's disease on the basis of limited objective information about the state of the patient's gastrointestinal tissue while aiming to achieve mucosal healing. Tools to predict changes in mucosal health with treatment are needed. We evaluated a computational approach integrating a mechanistic model of Crohn's disease with a responder classifier to predict temporal changes in mucosal health.