C-Procgen: Empowering Procgen with Controllable Contexts

We present C-Procgen, an enhanced suite of environments on top of the Procgen benchmark. C-Procgen provides access to over 200 unique game contexts across 16 games. It allows for detailed configuration of environments, ranging from game mechanics to agent attributes. This makes the procedural generation process, previously a black-box in Procgen, more transparent and adaptable for various research needs.The upgrade enhances dynamic context management and individualized assignments, while maintaining computational efficiency. C-Procgen's controllable contexts make it applicable in diverse reinforcement learning research areas, such as learning dynamics analysis, curriculum learning, and transfer learning. We believe that C-Procgen will fill a gap in the current literature and offer a valuable toolkit for future works

Development and validation of a prediction model for postoperative ischemic stroke following total arch replacement and frozen elephant trunk under mild hypothermia

Background: Early identification of postoperative ischemic stroke among patients with acute DeBakey type I aortic dissection (ADIAD) is of great significance to taking timely effective treatment. We aimed to develop and validate a prediction model for postoperative ischemic stroke in ADIAD patients who underwent total arch replacement (TAR) and frozen elephant trunk (FET) under mild hypothermia. Methods: ADIAD patients who underwent TAR and FET between January 2017 and April 2023 were enrolled in our study. Preoperative and intraoperative variables were selected using pairwise comparisons, the Least Absolute Shrinkage and Selection Operator (LASSO), and logistic regression to construct a prediction model for postoperative ischemic stroke. The accuracy and calibration of the model were assessed using 1000 bootstrap resamples for internal validation, with the area under the receiver operating characteristic curve (AUC) and the Hosmer-Lemeshow test. The AUC was also used to evaluate the model's accuracy in the validation cohort. Results: The development cohort included 246 patients. The mean [standard deviation (SD)] age of patients in the cohort was 50.7 (11.2) years, 196 (79.7%) were men, and 22 (8.9%) were diagnosed with postoperative ischemic stroke. The validation cohort included 73 patients with a mean (SD) age of 52.5 (11.9) years, 58 (79.5%) were men and 3 (4.1%) were diagnosed with postoperative ischemic stroke. Three variables out of the initial 40 potential predictors were included in the final prediction model: the platelet count [odd ratio (OR), 0.992; 95% confidence interval (CI), 0.983–1.000], the presence of innominate artery dissection (OR, 3.400; 95% CI, 1.027–11.260), and the flow of selective cerebral perfusion (OR, 0.147; 95% CI, 0.046–0.469). The mean AUC in the development cohort was 0.77 (95% CI, 0.68–0.87), and calibration was checked with the Hosmer-Lemeshow test (P = 0.78). In the validation cohort, the AUC was 0.98 (95% CI, 0.94–1.00). A prediction model and a clinical impact curve were developed for practical purposes. Conclusions: In this study, we have developed a prediction model with competent discriminative ability and calibration. This model can be used for early assessment of the risk of postoperative ischemic stroke in patients with ADIAD following TAR and FET under mild hypothermia