4 results match your criteria: "University of Utah Spencer F. Eccles School of Medicine[Affiliation]"
The Impact of Allergen Avoidance in Burning Mouth Syndrome: A Case Series.
Dermatitis
January 2025
Department of Dermatology, Autoimmune Skin Diseases Clinic, University of Utah Spencer F. Eccles School of Medicine, Salt Lake City, Utah, USA.
The association between skin allergy testing and oral squamous cell carcinoma in oral lichen planus: a retrospective cohort study.
Arch Dermatol Res
January 2025
Department of Dermatology, Autoimmune Skin Diseases Clinic, University of Utah Spencer F. Eccles School of Medicine, 30 N Mario Capecchi Drive level 1 South, Salt Lake City, 84132, UT, USA.
There is a reported association between oral contact allergy and oral lichen planus (OLP). Likewise oral squamous cell carcinoma (oSCC) is associated with OLP. It is hypothesized that chronic inflammation may contribute to oSCC risk.
View Article and Find Full Text PDFDisparities in Provider Ordering Practices of Image-Guided Interventions and Surgery for Patients With Low Back Pain: A Cohort Study.
J Am Coll Radiol
July 2024
Professor, Department of Radiology, University of Michigan, Ann Arbor, Michigan; and Editor-in-Chief of the JACR.
Article Synopsis
- The study looked at why some patients with low back pain (LBP) get certain treatments and others don’t, focusing on factors like where they live and their personal backgrounds.
- It found that people from poorer neighborhoods and some groups, like women and those without insurance, were less likely to get treatments for LBP.
- The conclusion was that a person's neighborhood and personal details, like their sex and financial situation, affect the kind of medical care they receive for low back pain.
The Pediatric Data Science and Analytics Subgroup of the Pediatric Acute Lung Injury and Sepsis Investigators Network: Use of Supervised Machine Learning Applications in Pediatric Critical Care Medicine Research.
Pediatr Crit Care Med
April 2024
Department of Pediatrics, University of Virginia School of Medicine, Charlottesville, VA.
Objective: Perform a scoping review of supervised machine learning in pediatric critical care to identify published applications, methodologies, and implementation frequency to inform best practices for the development, validation, and reporting of predictive models in pediatric critical care.
Design: Scoping review and expert opinion.
Setting: We queried CINAHL Plus with Full Text (EBSCO), Cochrane Library (Wiley), Embase (Elsevier), Ovid Medline, and PubMed for articles published between 2000 and 2022 related to machine learning concepts and pediatric critical illness.