Many national reports have called for undergraduate biology education to incorporate research and analytical thinking into the curriculum. In response, interventions have been developed and tested. CREATE ( C onsider, R ead, E lucidate the hypotheses, A nalyze and interpret the data, and T hink of the next E xperiment) is an instructional strategy designed to engage students in learning core concepts and competencies through careful reading of primary literature in a scaffolded fashion. CREATE has been successfully implemented by many instructors across diverse institutional contexts and has been shown to help students develop in the affective, cognitive, and epistemological domains, consistent with broader meta-analyses demonstrating the effectiveness of active learning. Nonetheless, some studies on CREATE have reported discrepant results, raising important questions on effectiveness in relation to the fidelity and integrity of implementation. Here, we describe an upper-division genetics course that incorporates a modified version of CREATE. Similar to the original CREATE instructional strategy, our intervention’s design was based on existing learning principles. Using existing concept inventories and validated survey instruments, we found that our modified CREATE intervention promotes higher affective and cognitive gains in students in contrast to three comparison groups. We also found that students tended to underpredict their learning and performance in the modified CREATE intervention, while students in some comparison groups had the opposite trend. Together, our results contribute to the expanding literature on how and why different implementations of the same active-learning strategy contribute to student outcomes.
BACKGROUND: Preoperative risk stratification for hepatectomy patients can aid clinical decision making. The objective of this retrospective cohort study was to determine postoperative mortality risk factors and develop a score-based risk calculator using a limited number of preoperative predictors to estimate mortality risk in patients undergoing hepatectomy. METHODS: Data were collected from patients that underwent hepatectomy from the National Surgical Quality Improvement Program dataset from 2014 to 2020. Baseline characteristics were compared between survival and 30-day mortality cohorts using the χ2 test. Next, the data were split into a training set to build the model and a test set to validate the model. A multivariable logistic regression model modeling 30-day postoperative mortality was trained on the training set using all available features. Next, a risk calculator using preoperative features was developed for 30-day mortality. The results of this model were converted into a score-based risk calculator. A point-based risk calculator was developed that predicted 30-day postoperative mortality in patients who underwent hepatectomy surgery. RESULTS: The final dataset included 38,561 patients who underwent hepatectomy. The data were then split into a training set from 2014 to 2018 (n = 26,397) and test set from 2019 to 2020 (n = 12,164). Nine independent variables associated with postoperative mortality were identified and included age, diabetes, sex, sodium, albumin, bilirubin, serum glutamic-oxaloacetic transaminase (SGOT), international normalized ratio, and American Society of Anesthesiologists classification score. Each of these features were then assigned points for a risk calculator based on their odds ratio. A univariate logistic regression model using total points as independent variables were trained on the training set and then validated on the test set. The area under the receiver operating characteristics curve on the test set was 0.719 (95% confidence interval, 0.681–0.757). CONCLUSIONS: Development of risk calculators may potentially allow surgical and anesthesia providers to provide a more transparent plan to support patients planned for hepatectomy.
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