ABSTRACT
Venous stasis ulcers are nonhealing lesions due to venous hypertension secondary to valvular dysfunction or deep venous outflow obstruction. We describe a case of a 71-year-old male with a history of polycythemia vera, secondary myelofibrosis, and massive splenomegaly up to 38 cm who presented with chronic, perimalleolar venous stasis ulcers and pain on the left lower extremity. CT showed significant compression of the left common iliac vein due to mass effect from the spleen. He was managed medically while being evaluated for partial splenic artery embolization but expired due to other chronic conditions before any intervention could be performed. Partial splenic artery embolization may be considered as a treatment option for patients with symptomatic iliac vein compression due to massive splenomegaly secondary to myelofibrosis, as long as extramedullary hematopoiesis is not compromised.
ABSTRACT
Mind wandering is often characterized by attention oriented away from an external task towards our internal, self-generated thoughts. This universal phenomenon has been linked to numerous disruptive functional outcomes, including performance errors and negative affect. Despite its prevalence and impact, studies to date have yet to identify robust behavioral signatures, making unobtrusive, yet reliable detection of mind wandering a difficult but important task for future applications. Here we examined whether electrophysiological measures can be used in machine learning models to accurately predict mind wandering states. We recorded scalp EEG from participants as they performed an auditory target detection task and self-reported whether they were on task or mind wandering. We successfully classified attention states both within (person-dependent) and across (person-independent) individuals using event-related potential (ERP) measures. Non-linear and linear machine learning models detected mind wandering above-chance within subjects: support vector machine (AUC = 0.715) and logistic regression (AUC = 0.635). Importantly, these models also generalized across subjects: support vector machine (AUC = 0.613) and logistic regression (AUC = 0.609), suggesting we can reliably predict a given individual's attention state based on ERP patterns observed in the group. This study is the first to demonstrate that machine learning models can generalize to "never-seen-before" individuals using electrophysiological measures, highlighting their potential for real-time prediction of covert attention states.
