Using Natural Language Processing to Automatically Assess Feedback Quality: Findings From 3 Surgical Residencies.

Purpose: Learning is markedly improved with high-quality feedback, yet assuring the quality of feedback is difficult to achieve at scale. Natural language processing (NLP) algorithms may be useful in this context as they can automatically classify large volumes of narrative data. However, it is unknown if NLP models can accurately evaluate surgical trainee feedback.

Association of cigarette smoking and adverse events in left ventricular assist device patients.

Introduction: Adverse events (AEs) associated with left ventricular assist devices (LVADs) cause significant morbidity and mortality. Little is known about patient-specific factors that contribute to rates of AEs. The purpose of this study was to assess the association of cigarette smoking history and AEs following LVAD implantation.