The patient-physician relationship can be understood from the two aspects of patients′ trust and patients′ satisfaction.Compared with patients′ satisfaction,dimensions and attributes of patients′ trust indicate to better match with the measuring capacity of patients.Thus,the evaluation of the quality of medical services based on patients′ trust is justified theoretically.The existing literatures assert that the research on patients′ trust could help understand patient-physician relationship and eventually improve the quality of medical services.The conceptual model in this paper entails the concept,dimensions and attributes of patients′ trust.Also,this paper points out its effect on the measurement of the quality of medical services through the comparison of patients′ trust and patients′ satisfaction.

Objective To construct and investigate the multi-leaf collimator (MLC) fault prediction model of Varian NovalisTx medical linear accelerator based on BP neural network.Methods The MLC fault data applied in clinical trial for 18 months were collected and analyzed.The total use time of accelerator,the quantity of patients per month,average daily working hours of accelerator,volume of RapidArc plans and time interval between accelerator maintenance were used as the input factors and the prediction of MLC fault frequency was considered as the output result.The BP neural network model of MLC fault prediction was realized by AMORE package of R language and the simulation results were validated.Results The model contained 3 layers of network to realize the input-output switch.There were 5 nodes in the input layer,13 nodes in the hide layer and 1 node in the output layer,respectively.The transfer function from the input layer to the hide layer selected the tansig function and purelin function was used from the hide layer to the output layer.The maximum time of training was pre-set as 150 in the designed model.Actually,111 times of training were performed.The pre-set error was 3％ and the actual error was 2.7％,which indicated good convergence.The simulation results of MLC fault applied in clinical trial for 18 months were similar to the actual data.Conclusions The BP neural network model realized by R language of MLC fault prediction can describe the mapping relationship between fault factors and fault frequency,which provides references for the understanding of accelerator fault and management of spare parts inventory.

Objective:To evaluate partial splenectomy (LPS) in the treatment of benign solid tumors of the spleen.Methods:The clinical data of patients with benign solid tumors of spleen treated by laparoscopy from Jan 2010 to Jun 2018 in the Third Affiliated Hospital of Soochow University was retrospectively analyzed. Patients were divided into LPS group and laparoscopic total splenectomy (LTS) group.Results:There were 21 cases in LPS group and 25 cases in LTS group. Differences between the two groups, operative time, blood loss, transfusion rate, maintenance of drain, postoperative hospital stay, costs, postoperative WBC and platelet count, and postoperative complications such as hemorrhage, fever, splenic fossa effusion, pancreatic fistula, venous thrombosis were statistically insignificant. However, the postoperative incidence of thrombocythemia in the LPS group were significantly lower compared to the LTS group (χ 2 =4.293, P<0.05). Conclusions:Patients with benign solid tumors of the spleen will benefit more from LPS compared to LTS.