https://www.selleckchem.com/products/muvalaplin.html the included studies. During extraction, we will record information on the design characteristics of the studies, the theories of motivation they are informed by, the motivational constructs they target, and the mediators and moderators they consider. We have executed our database and registry searches and have begun screening titles and abstracts. By appraising the characteristics of studies that have focused on the motivational design of web-based instruction in HPE, the planned review will produce recommendations that will ensure impactful programs of future research in this crucial educational space. PROSPERO CRD42022359521; https//tinyurl.com/57chuzf6. DERR1-10.2196/42681. DERR1-10.2196/42681. Clinical prediction models suffer from performance drift as the patient population shifts over time. There is a great need for model updating approaches or modeling frameworks that can effectively use the old and new data. Based on the paradigm of transfer learning, we aimed to develop a novel modeling framework that transfers old knowledge to the new environment for prediction tasks, and contributes to performance drift correction. The proposed predictive modeling framework maintains a logistic regression-based stacking ensemble of 2 gradient boosting machine (GBM) models representing old and new knowledge learned from old and new data, respectively (referred to as transfer learning gradient boosting machine [TransferGBM]). The ensemble learning procedure can dynamically balance the old and new knowledge. Using 2010-2017 electronic health record data on a retrospective cohort of 141,696 patients, we validated TransferGBM for hospital-acquired acute kidney injury prediction. The baseline models (ie, transported models) that were trained on 2010 and 2011 data showed significant performance drift in the temporal validation with 2012-2017 data. Refitting these models using updated samples resulted in performance gains in n