Objective:To analyze the influencing factors of low fall alertness in elderly inpatients,construct a risk prediction model and validate it,providing a reference for clinical medical staff to identify elderly inpatients with low fall alertness in the early stage.Methods:A total of 605 elderly inpatients treated in Yijishan Hospital affiliated to Wannan Medical College from Oct 2023 to Mar 2024 were enrolled and randomly divided into the training group(n=423)and validation group(n=182)at a ratio of 7∶3.The patients were evaluated using a general information questionnaire,the Social Frailty Screening Tool(HALFT),the Tilburg Frailty Indicator(TFI),and the Self-Awareness of Falls in Elderly scale(SAFE).Multivariate logistic analysis was used to determine the influencing factors of low fall alertness in elderly inpatients.RStudio was used to construct a risk prediction model of low fall alertness.The discrimination,calibration,and clinical net benefit of the model were verified using the receiver operating characteristic(ROC)curves,calibration plots,and decision curve analysis(DCA).Results:Multivariate logistic analysis showed that the history of falls,monthly income,previous physical activity time,social frailty score and TFI score were independent risk factors for low fall alertness in elderly inpatients.The Hosmer-Lemeshow χ2 test showed that χ2=8.863,P=0.354,indicating good calibration of the prediction model.The area under the ROC curve of the training group and the validation group were 0.860(95%CI:0.815-0.904)and 0.937(95%CI:0.888-0.986),respectively,and the maximum Youden indices of the model was 0.576 and 0.788,respectively,indicating good discrimination of the model.The DCA decision curve showed that the model had good clinical effectiveness.Conclusion:The constructed model has a good prediction effect and can help clinical medical staff quickly and effectively screen out elderly inpatients at risk of low fall alertness.

Objective:To analyze the influencing factors of low fall alertness in elderly inpatients,construct a risk prediction model and validate it,providing a reference for clinical medical staff to identify elderly inpatients with low fall alertness in the early stage.Methods:A total of 605 elderly inpatients treated in Yijishan Hospital affiliated to Wannan Medical College from Oct 2023 to Mar 2024 were enrolled and randomly divided into the training group(n=423)and validation group(n=182)at a ratio of 7∶3.The patients were evaluated using a general information questionnaire,the Social Frailty Screening Tool(HALFT),the Tilburg Frailty Indicator(TFI),and the Self-Awareness of Falls in Elderly scale(SAFE).Multivariate logistic analysis was used to determine the influencing factors of low fall alertness in elderly inpatients.RStudio was used to construct a risk prediction model of low fall alertness.The discrimination,calibration,and clinical net benefit of the model were verified using the receiver operating characteristic(ROC)curves,calibration plots,and decision curve analysis(DCA).Results:Multivariate logistic analysis showed that the history of falls,monthly income,previous physical activity time,social frailty score and TFI score were independent risk factors for low fall alertness in elderly inpatients.The Hosmer-Lemeshow χ2 test showed that χ2=8.863,P=0.354,indicating good calibration of the prediction model.The area under the ROC curve of the training group and the validation group were 0.860(95%CI:0.815-0.904)and 0.937(95%CI:0.888-0.986),respectively,and the maximum Youden indices of the model was 0.576 and 0.788,respectively,indicating good discrimination of the model.The DCA decision curve showed that the model had good clinical effectiveness.Conclusion:The constructed model has a good prediction effect and can help clinical medical staff quickly and effectively screen out elderly inpatients at risk of low fall alertness.

Objective:To investigate the intervention effect of binary coping strategy based on systemic interaction model on the postoperative survival quality of patients with oral cancer.Methods:A total of 99 patients with oral cancer admitted to the Department of Oral and Maxillofacial Surgery of a tertiary hospital from Jun 2021 to Jun 2022 was selected.They were randomly divided into the control group(50 cases)and the observation group(49 cases)with random number table method.The control group received routine nursing for oral cancer.On this basis,the observation group also received the binary coping strategy based on the systemic interaction model.The scores of UW-QOL quality of life scale and binary coping scale were compared between the two groups before surgery,at the 3rd and 9th weeks after surgery.Results:The UW-QOL scores of both groups at the 3rd and 9th weeks after surgery were lower than those at admission,and the UW-QOL score in the control group was lower than that in the observation group(P＜0.05).At the 3rd week after surgery,the score of coping with the partner in the observation group was higher than that in the control group(P＜0.05).At the 9th week after surgery,the total score,negative coping,stress communication,coping together,and supportive coping scores in the observation group were higher than those in the control group(P＜0.05).Repeated measures analysis of variance showed that there was an interaction between time and group for the total score of binary coping scale(P＜0.05).And there was a significant main effect of time and group on the total score of binary coping scale(P＜0.05).Conclusions:The quality of life of patients with oral cancer is poor.The binary coping strategy based on the systemic interaction model can improve the quality of life of patients,enhance the intimacy of patients with their partners,and contribute to the disease recovery of patients with oral cancer.