Is a Venacavogram Necessary after Inferior Vena Cava Filter Retrieval?

PURPOSE: To evaluate the utility of venacavography after optional inferior vena cava (IVC) filter retrieval using routine and complex techniques. MATERIALS AND METHODS: Patients (N = 224; 228 patient encounters) in whom venacavography was performed before and after IVC filter retrieval were reviewed from a 12-year period ending March 2014. Retrieval was considered complex if it required forceps or other adjuncts. Venacavograms were assessed for abnormalities including stenosis, filling defects, dissection, and extravasation of contrast material. RESULTS: Filling defects (n = 53; 23%) and stenosis (n = 137; 60%) were significantly more frequent after retrieval (P < .05), but they did not change patient management. The only major abnormality after retrieval was extravasation (n = 3), which occurred only in the complex group and was treated with balloon tamponade and observation. No dissection was observed. Dwell time was not correlated with the presence of abnormalities after retrieval (r = 0.002, P = .977). CONCLUSIONS: The lack of abnormalities requiring treatment on venacavography after routine IVC filter retrieval may justify omitting venacavography after retrieval regardless of dwell time. Although uncommon, extravasation requiring treatment may be seen after complex retrieval; venacavography remains warranted in this setting.

An artificial intelligence approach for selecting effective teacher communication strategies in autism education.

Effective inclusive education is key in promoting the long-term outcomes of children with autism spectrum conditions (ASC). However, no concrete consensus exists to guide teacher-student interactions in the classroom. In this work, we explore the potential of artificial intelligence as an approach in autism education to assist teachers in effective practice in developing social and educational outcomes for children with ASC. We form a protocol to systematically capture such interactions, and conduct a statistical analysis to uncover basic patterns in the collected observations, including the longer-term effect of specific teacher communication strategies on student response. In addition, we deploy machine learning techniques to predict student response given the form of communication used by teachers under specific classroom conditions and in relation to specified student attributes. Our analysis, drawn on a sample of 5460 coded interactions between teachers and seven students, sheds light on the varying effectiveness of different communication strategies and demonstrates the potential of this approach in making a contribution to autism education.