Journal of the American College of Surgeons
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Recent large retrospective studies suggest that breast-conserving therapy (BCT) plus radiation yielded better outcomes than mastectomy (MST) for women with early-stage breast cancer (ESBC). Whether this is applicable to the different subtypes is unknown. We hypothesize that BCT yielded better outcomes than MST, regardless of subtypes of ESBC. ⋯ BCT yielded better survival rates than mastectomy for women with all subtypes of ESBC. The role of mastectomy for women with ESBC should be reassessed in future clinical trials.
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Clinical trial participation among cancer patients remains low. We sought to examine the impact of patient- and system-level factors on clinical trial participation among gastrointestinal (GI) surgical patients. ⋯ Clinical trial participation is low among adult GI cancer patients who undergo surgery in the US. Programs aimed at improving trial participation among vulnerable populations are needed to improve trial participation.
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Nearly 1 in 5 medical students reports at least 1 incident of mistreatment, with many occurring in the perioperative environment. We aimed to further define the types of mistreatment occurring perioperatively in a national data set by using a mixed-methods approach. ⋯ A significant proportion of medical student mistreatment events occur in the context of surgery. Surgeons and trainees must play active roles in leading and instituting needed changes to improve the learning environment to support medical students and recruit a sufficient future surgical workforce.
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The introduction of more effective chemotherapy a decade ago has led to increased use of neoadjuvant therapy (NAT) in patients with pancreatic ductal adenocarcinoma (PDAC). The aim of this study was to assess the evolving use of NAT in individuals with PDAC undergoing pancreatoduodenectomy (PD) and to compare their outcomes with patients undergoing upfront operation. ⋯ NAT before pancreatoduodenectomy increased more than 3-fold over the past decade and was associated with improved optimal operative outcomes.
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The American College of Surgeons (ACS) NSQIP risk calculator helps guide operative decision making. In patients with significant surgical risk, it may be unclear whether to proceed with "Hail Mary"-type interventions. To refine predictions, a local interpretable model-agnostic explanations machine (LIME) learning algorithm was explored to determine weighted patient-specific factors' contribution to mortality. ⋯ Through the application of machine learning algorithms (GBM and LIME), our model individualized predicted mortality and contributing factors with substantial ACS-NSQIP predicted mortality. USE of machine learning techniques may better inform operative decisions and family conversations in cases of significant surgical risk.