Career adapt-ability scale -short form: Validation among early career stage of Chinese university graduates.

The Career Adapt-Ability Scale (CAAS) is the favored method among researchers for measuring career adaptability. The 12-item version of CAAS-SF, which was made by Maggiori, Rossier, and Savickas based on a change to CAAS, has been slowly used by different groups in different countries and regions. As samples for the validation of the scale in this study, 571 Chinese university graduates in the early stages of their professions were chosen. Principal component analysis and confirmatory factor analysis suggest that CAAS-SF and CAAS have very similar psychological measurement features and factor structures. And the internal consistency of each subscale and total scale are equivalent to or greater than that of the CAAS assessment. These findings indicate that the CAAS-SF is a valid and reliable instrument for evaluating China's career adaptability. In addition, limitations, issues for further research, and suggestions are emphasized.

Performance of Deep Learning-Based Algorithm for Detection of Pediatric Intussusception on Abdominal Ultrasound Images.

Background and Aims: Diagnosing pediatric intussusception from ultrasound images can be a difficult task in many primary care hospitals that lack experienced radiologists. To address this challenge, this study developed an artificial intelligence- (AI-) based system for automatic detection of "concentric circles" signs on ultrasound images, thereby improving the efficiency and accuracy of pediatric intussusception diagnosis. Methods: A total of 440 cases (373 pediatric intussusception and 67 normal cases) were retrospectively collected from Children's Hospital affiliated to Zhejiang University School of Medicine from January 2020 to December 2020. An improved Faster RCNN deep learning framework was used to detect "concentric circle" signs. Finally, independent validation set was used to evaluate the performance of the developed AI tool. Results: The data of pediatric intussusception were divided into a training set and validation set according to the ratio of 8 : 2, with training set (298 pediatric intussusception) and validation set (75 pediatric intussusception and 67 normal cases). In the "concentric circle" detection model, the detection rate, recall, specificity, and F1 score assessed by the validation set were 92.8%, 95.0%, 92.2%, and 86.4%, respectively. Pediatric intussusception was classified by "concentric circle" signs, and the accuracy, recall, specificity, and F1 score were 93.0%, 92.0%, 94.1%, and 93.2% on the validation set, respectively. Conclusion: The model established in this paper can realize the automatic detection of "concentric circle" signs in the ultrasound images of abdominal intussusception in children; the AI tool can improve the diagnosis speed of pediatric intussusception. It is necessary to further develop an artificial intelligence system for real-time detection of "concentric circles" in ultrasound images for the judgment of children with intussusception.

[Echocardiography in diagnosis of pediatric pulmonary artery sling].

OBJECTIVE: To evaluate the application of echocardiography in diagnosis of pediatric pulmonary artery sling. METHODS: Twenty-five children diagnosed as pulmonary artery sling in the Children's Hospital, Zhejiang University School of Medicine from July 2012 to August 2017, and 50 healthy children(control group) were enrolled in the study. Echocardiography was performed in all subjects. The origins of the left pulmonary artery(LPA) and right pulmonary artery(RPA) were observed; the internal diameters of LPA, RPA and main pulmonary artery(MPA) were measured; and the ratios of LPA/MPA and RPA/MPA were calculated. The value of RPA/MPA and LPA/MPA in diagnosing pulmonary artery sling was evaluated by ROC curve. RESULTS: Echocardiography showed that the internal diameters of RPA were increased in all patients; while the LPA and blood flow signals in common pulmonary arterial bifurcation were not found in 24 cases, in whom the MPA was directly extended to the RPA, and the LPA was from the massive RPA. The ultrasound of one special case showed that there was no distinct bifurcation of MPA; the MPA walked to the right and then to the left with no change in the internal diameter. There were significant differences in RPA/MPA(0.50±0.05 vs. 0.71±0.15, t=7.06, P<0.01) and LPA/MPA(0.52±0.05 vs. 0.39±0.09, t=6.94, P<0.01) between controls and the patients. ROC curve analysis showed that the AUC of RPA/MPA and LPA/MPA in diagnosis of pulmonary artery sling were 0.90 and 0.89, respectively. When taking 0.60 as the cutoff value, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy of RPA/MPA in diagnosis of pulmonary artery sling were 79%, 100%, 100%, 91% and 93%, respectively. When taking 0.43 as the cutoff value, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy of LPA/MPA in diagnosis of pulmonary artery sling were 71%, 96%, 89%, 87% and 88%, respectively. CONCLUSIONS: s Echocardiography can effectively diagnose pediatric pulmonary artery sling. The RPA/MPA ratio of 0.60 and the LPA/MPA ratio of 0.43 can be used as cutoff values for diagnosis.