Construction of a predictive model for cognitive impairment risk in patients with advanced cancer.

AIMS: The purpose of this study was to identify risk factors for cognitive impairment in advanced cancer patients and to develop predictive models based on these risk factors. BACKGROUND: Cancer-related cognitive impairment seriously affects the quality of life of advanced cancer patients. However, neural network models of cognitive impairment in patients with advanced cancer have not yet been identified. DESIGN: A cross-sectional design was used. METHODS: This study collected 494 questionnaires between January and June 2022. Statistically significant clinical indicators were selected by univariate analysis, and the artificial neural network model and logistic regression model were used for multivariate analysis. The predicted value of the model was estimated using the area under the subject's working characteristic curve. RESULT: The artificial neural network and the logistic regression models suggested that cancer course, anxiety and age were the major risk factors for cognitive impairment in advanced cancer patients. All the indexes of artificial neural network model constructed in this study are better than those of the logistic model. CONCLUSION: The artificial neural network model can better predict the risk factors of cognitive impairment in patients with advanced cancer. Better prediction will enable nurses and other healthcare professionals to provide better targeted and timely support.

Research on Employment Quality Evaluation Index System of Medical Students based on Analytic Hierarchy Process / 中华医学教育探索杂志

Objective:To construct medical students' employment quality evaluation index system based on analytic hierarchy process (AHP), for providing basis to scientific and objective evaluation of medical students' employment quality.Methods:Two rounds of consultation with 21 experts were conducted to construct medical students' employment quality index and evaluation standard by Delphi method, and the weight of each index and evaluation standard determined by AHP. Excel 2007 and SPSS 21.0 were used to analyze the results of expert consultation. The enumeration data were expressed as frequency and percentage. The mean and coefficient of variation were used to describe the importance scores of experts on indicators at all levels. The positive coefficient, authority coefficient and coordination degree of experts were calculated, and the Kendall coordination coefficient ( W) test was carried out. Yaahp 6.0 is used to analyze the pairwise comparison matrix in the analytic hierarchy process to calculate the weight of the indicator. Results:The authority of expert consultation was ranged from 0.77 to 0.94, and the positive coefficient of experts was 100.00%. The evaluation system of medical students' employment quality was determined, which involved 3 first-class indexes, 9 second-class indexes and 35 third-class indexes, and the weight coefficients of each index were calculated by AHP.Conclusion:The evaluation index system of medical students' employment quality is reasonable, which can be used to provide reference standard for medical students' employment quality evaluation, and has certain application value.