Background: In response to the COVID-19 pandemic, the Yale New Haven Health System began rescheduling nonurgent outpatient appointments as virtual visits in March 2020. While Yale New Haven Health expanded its telemedicine infrastructure to accommodate this shift, many appointments were delayed and patients faced considerable uncertainty.
Objective: Medical students created the Medical Student Task Force (MSTF) to help ensure continuity of care by calling patients whose appointments were delayed during this transition to telemedicine.
Purpose: The medical literature relevant to germline genetics is growing exponentially. Clinicians need tools that help to monitor and prioritize the literature to understand the clinical implications of pathogenic genetic variants. We developed and evaluated two machine learning models to classify abstracts as relevant to the penetrance-risk of cancer for germline mutation carriers-or prevalence of germline genetic mutations.
View Article and Find Full Text PDFPurpose: Quantifying the risk of cancer associated with pathogenic mutations in germline cancer susceptibility genes-that is, penetrance-enables the personalization of preventive management strategies. Conducting a meta-analysis is the best way to obtain robust risk estimates. We have previously developed a natural language processing (NLP) -based abstract classifier which classifies abstracts as relevant to penetrance, prevalence of mutations, both, or neither.
View Article and Find Full Text PDFScientific evidence validating the beneficial effect of loupes in preventing musculoskeletal disorders is very scarce. The aim of this study was to assess the impact of dental loupes on dental students' posture during a preclinical restorative dentistry course. Using a randomized crossover design, this study was conducted at the School of Dentistry, University of Nantes, France, in 2017.
View Article and Find Full Text PDF