Objective:To explore the application of PDCA cycle combined with project management method in the assessment and management of special skills training in clinical skills center.Methods:From January to December in 2018, the PDCA cycle combined with project management method was used to manage the special skills training project team. The utilization rate of special skills simulation equipment before and after management was compared. A questionnaire survey was conducted to investigate the satisfaction with the management model among the students involved in the special skills training in 2018.The data was analyzed by SPSS 13.0.Results:The utilization rate of special skills simulation equipment was significantly improved, and the students were highly satisfied with the management model.Conclusion:Using PDCA cycle combined with project management method, this paper explores a management model of special skills training and assessment in clinical skills center, which improves the quality of special skills training and assessment management in clinical skills center, and is worthy of further exploration and practice.

Objective:To investigate the relationship between spread through air spaces(STAS) of peripheral stage ⅠA small adenocarcinoma of the lung(≤2 cm) and related factors such as clinical and CT morphological features, and to construct a nomogram model.Methods:Relevant clinical, pathological and imaging data of patients who underwent lung surgery and were diagnosed as peripheral stage ⅠA small lung adenocarcinoma by postoperative pathology in the Affiliated Hospital of Nantong University from 2017 to 2022 were collected, of which cases that met the inclusion criteria from 2017 to 2021 served as the training group, and those that met the inclusion criteria in 2022 served as the validation group. The independent risk factors for the occurrence of STAS in peripheral stage ⅠA lung small adenocarcinoma were investigated by using univariate analysis and multifactorial logistic regression analysis, based on which a nomogram prediction model was constructed, and the subjects were analyzed by using the receiver operating characteristic curve( ROC), correction model, etc. were used to evaluate the model. Results:A total of 430 patients who met the criteria were included, including 351 patients in the training group(109 STAS-positive and 242 STAS-negative) and 79 patients in the validation group(23 STAS-positive and 56 STAS-negative). Univariate analysis showed that the patients in the two groups showed a significant difference in age(>58 years old), gender, smoking history, tumor location(subpleural, non-subpleural), pleural pull, nodule type, nodule maximal diameter, solid component maximal diameter, consolidation tumor ratio(CTR), lobulation sign, burr sign, bronchial truncation sign, vascular sign(includes thickening and distortion of blood vessels in/around the nodes), satellite lesions, and ground-glass band sign were statistically significant( P<0.05). The results of multifactorial logistic regression analysis showed that CTR( OR=4.98, P<0.001), lobulation sign( OR=4.07, P=0.013), burr sign( OR=3.66, P<0.001), and satellite lesions( OR=3.56, P=0.009) were the independent risk factors for the occurrence of STAS. Applying the above factors to construct the nomogram model and validate the model, the results showed that the ROC curve was plotted by the nomogram prediction model, and the area under the ROC curve( AUC) of the training set was 0.840(sensitivity 0.835, specificity 0.734), and the validation set had an AUC value of 0.852(sensitivity 0.786, specificity 0.783), and the training set and validation set calibration curves have good overlap with the ideal curve. Conclusion:CTR, lobular sign, burr sign, and satellite lesions are independent risk factors for STAS, and the nomogram model constructed in this study has good predictive value.