(1) Background: A tophus is a clinical manifestation of advanced gout, and in some patients could lead to joint deformities, fractures, and even serious complications in unusual sites. Therefore, to explore the factors related to the occurrence of tophi and establish a prediction model is clinically significant. (2) Objective: to study the occurrence of tophi in patients with gout and to construct a predictive model to evaluate its predictive efficacy. (3) Methods: The clinical data of 702 gout patients were analyzed by using cross-sectional data of North Sichuan Medical College. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were used to analyze predictors. Multiple machine learning (ML) classification models are integrated to analyze and identify the optimal model, and Shapley Additive exPlanations (SHAP) interpretation was developed for personalized risk assessment. (4) Results: Compliance of urate-lowering therapy (ULT), Body Mass Index (BMI), course of disease, annual attack frequency, polyjoint involvement, history of drinking, family history of gout, estimated glomerular filtration rate (eGFR), and erythrocyte sedimentation rate (ESR) were the predictors of the occurrence of tophi. Logistic classification model was the optimal model, test set area under curve (AUC) (95% confidence interval, CI): 0.888 (0.839–0.937), accuracy: 0.763, sensitivity: 0.852, and specificity: 0.803. (5) Conclusions: We constructed a logistic regression model and explained it with the SHAP method, providing evidence for preventing tophus and guidance for individual treatment of different patients.
The carbon emission trading mechanism is an environmental regulation that has both market and government orientations and has a significant impact on the innovation of green technology and low-carbon development. Based on the evolutionary game theory and considering the strategic choices of different enterprise types in the carbon trading market, a three-party game model, involving enterprise A, the government, and enterprise B, is constructed. Based on data on the carbon emission trading market, data simulation is used to analyze the evolutionary game trajectory of government and enterprise behavior strategies. This study finds that 1) carbon prices, additional green technology innovation benefits, and innovation incentives have a significant impact on corporate strategy choices, as with higher carbon prices, additional benefits, and greater innovation incentives, green technology innovation can compensate for corporate innovation investment enterprises tending to choose innovative strategies; 2) enterprises with different innovation inputs and outputs have different strategic choices under identical conditions, such as small enterprise B having higher input and lower output than large enterprise A, and therefore, when the government encourages policies such as innovation subsidies, it must treat different types of enterprises differently; and 3) the cost of supervision and punishment can help avoid behaviors such as “floating green” and “fraudulent compensation”, but enterprises and the supervision strategy of the government are affected by the associated supervision cost. This study not only further verifies the Porter hypothesis in both theory and practice but also has important implications for corporate green innovation strategies and government regulatory behavior while providing a reference for the carbon emission trading market and corporate low-carbon development.
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