While adherence to long-term follow-up after bariatric surgery is a mandate for center of excellence certification, the effect of attrition on weight loss is not well understood. The aim of this study was to assess the effect of postoperative follow-up on 12-month weight loss using the Bariatric Outcomes Longitudinal Database (BOLD) dataset. Patients with complete follow-up (3, 6, and 12 months) were compared to patients who had one or more prior missed visits. There were 51,081 patients with 12-month follow-up data available. After controlling for baseline characteristics, complete follow-up was independently associated with excess weight loss ≥50%, and total weight loss ≥30%. Adherence to postoperative follow-up is independently associated with improved 12-month weight loss after bariatric surgery. Bariatric programs should strive to achieve complete follow-up for all patients.
High SG volume is associated with improved 30-day readmission, reoperation, and complication rates. Concurrent RYGB volume impacts the 30-day complication rate after SG, but does not affect the readmission or reoperation rate. Our findings suggest that SG-specific volume is important for optimal safety outcomes in SG patients.
Readmission following LRYGB is significantly associated with surgeon operative volume; surgeons that perform fewer than 50 LRYGB per year are more likely to have 30-day readmissions and complications. Our findings support other more generalized studies suggesting surgeon case volume is inversely associated with increased risk of adverse outcomes and complications. As such, performance of LRYGB by HVS may decrease patient morbidity, hospital readmission, and overall healthcare utilization.
Deep Neural Networks (DNNs) are popularly used for implementing autonomy related tasks in automotive Cyber-Physical Systems (CPSs). However, these networks have been shown to make erroneous predictions to anomalous inputs, which manifests either due to Out-of-Distribution (OOD) data or adversarial attacks. To detect these anomalies, a separate DNN called assurance monitor is often trained and used in parallel to the controller DNN, increasing the resource burden and latency. We hypothesize that a single network that can perform controller predictions and anomaly detection is necessary to reduce the resource requirements. Deep-Radial Basis Function (RBF) networks provide a rejection class alongside the class predictions, which can be utilized for detecting anomalies at runtime. However, the use of RBF activation functions limits the applicability of these networks to only classification tasks. In this paper, we show how the deep-RBF network can be used for detecting anomalies in CPS regression tasks such as continuous steering predictions. Further, we design deep-RBF networks using popular DNNs such as NVIDIA DAVE-II, and ResNet20, and then use the resulting rejection class for detecting adversarial attacks such as a physical attack and data poison attack. Finally, we evaluate these attacks and the trained deep-RBF networks using a hardware CPS testbed called DeepNNCar and a real-world German Traffic Sign Benchmark (GTSB) dataset. Our results show that the deep-RBF networks can robustly detect these attacks in a short time without additional resource requirements.
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