Objective: Women remain a minority of trainees in interventional radiology (IR) since the residency's inception in 2014. Similar phenomena have been observed in other surgical specialties. Our study aims to quantify changes in female trainee representation in integrated IR over a five-year period from the 2018-19 to 2022-23 academic years and to compare with trends in other specialties.
View Article and Find Full Text PDFPurpose: To provide evidence-based guidance for clinicians who treat patients with stage I-III anal cancer.
Methods: A systematic review of the literature conducted by the Minnesota Evidence-based Practice Center provided the evidence base for this guideline. An ASCO Expert Panel reviewed this evidence and came to consensus on a set of evidence-based recommendations.
Objective: To investigate the value of anti-β2 glycoprotein-I domain 1 (aD1) and antiphosphatidylserine-prothrombin antibodies for predicting adverse pregnancy outcomes in an at-risk population and to describe the relationship among aD1, antiphosphatidylserine-prothrombin, lupus anticoagulant, and other antiphospholipid antibodies (aPL).
Methods: Data were obtained from a prospective cohort of pregnant patients with aPL, with systemic lupus erythematosus (SLE) (n=59) or without SLE (n=106), or SLE without aPL (n=100) (PROMISSE [Predictors of Pregnancy Outcome in Systemic Lupus Erythematosus and Antiphospholipid Syndrome] study; NCT00198068). Levels of aD1 and antiphosphatidylserine-prothrombin were quantified with the QUANTA Flash and QUANTA Lite systems, respectively, in sera collected at less than 18 weeks of gestation.
Pulmonary sequestration is a malformation of lung tissue such that a zone of pulmonary parenchyma exists in isolation from the bronchopulmonary tree. This condition is typically treated with surgical resection, but an increasing number of sequestrations are being treated with arterial embolization. We report interventions that were performed at two institutions on patients 53-70 years old.
View Article and Find Full Text PDFBackground And Aim: Endoscopic ultrasound (EUS) is the most sensitive method for evaluation of pancreatic lesions but is limited by significant operator dependency. Artificial intelligence (AI), in the form of computer-aided detection (CADe) systems, has shown potential in increasing accuracy and bridging operator dependency in several endoscopic domains. However, the complexity of integrating AI into EUS is far more challenging.
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