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Background: With potential of deep learning in musculoskeletal image interpretation being explored, this paper focuses on the common site of rotator cuff tears, the supraspinatus. It aims to propose and validate a deep learning model to automatically extract the supraspinatus, verifying its superiority through comparison with several classical image segmentation models. Method: Imaging data were retrospectively collected from 60 patients who underwent inpatient treatment for rotator cuff tears at a hospital between March 2021 and May 2023. A dataset of the supraspinatus from MRI was constructed after collecting, filtering, and manually annotating at the pixel level. This paper proposes a novel A-DAsppUnet network that can automatically extract the supraspinatus after training and optimization. The analysis of model performance is based on three evaluation metrics: precision, intersection over union, and Dice coefficient. Results: The experimental results demonstrate that the precision, intersection over union, and Dice coefficients of the proposed model are 99.20%, 83.38%, and 90.94%, respectively. Furthermore, the proposed model exhibited significant advantages over the compared models. Conclusion: The designed model in this paper accurately extracts the supraspinatus from MRI, and the extraction results are complete and continuous with clear boundaries. The feasibility of using deep learning methods for musculoskeletal extraction and assisting in clinical decision-making was verified. This research holds practical significance and application value in the field of utilizing artificial intelligence for assisting medical decision-making. [ABSTRACT FROM AUTHOR]

Background: Talent is a crucial resource for economic and social development, serving as the driving force behind urban progress. As China experiences rapid growth in digital city construction, the capability of e-services continues to improve incessantly. In China, the new-generation highly educated migrants (NGHEMs) account for ~ 20–30% of the total floating populations. This study aimed to explore the settlement intention of new-generation highly educated migrations in China from the new perspective of urban e-service capabilities. Furthermore, the mechanism of the urban e-services on the settlement intention on the NGHEMs will be proved. Methods: This paper employed data of China Migrants Dynamic Survey in 2017 and Evaluation Report of Government E-service Capability Index (2017). Descriptive analyses were conducted to investigate the factors influencing the settlement intention of NGHEMs in the destination city. Based on the principle of utility maximization, LASSO regression was employed to select individual and city characteristics that determined the settlement intention of NGHEMs. The impact of urban e-services on settlement intention was analyzed by using ordinal logit model. Additionally, robustness check, endogeneity analysis, and heterogeneity analysis were performed to validate the benchmark regression results. Finally, mediation model was employed to examine whether urban e-services enhance the settlement intention of NGHEMs by improving urban livability and urban innovation. Results: The results indicate that the urban e-services promote the NGHEMs' settlement intention in the destination cities. Moreover, the results are still robust through a series of robustness tests. Furthermore, from the perspective of individual and regional heterogeneity, urban e-services significantly enhances the settlement intention of NGHEMs with male and female, married and urban household registration, and urban e-services can promote the settlement intention of NGHEMs with over 3 million inhabitants and those in the eastern regions of China. Finally, the intermediary effect test shows that urban e-services promote the settlement intention of NGHEMs through urban livability and urban innovation. Conclusion: This study highlights the important impact of urban e-services on the settlement intention of new-generation highly educated migrants. The conclusions of this study provide suggestions for the government to use when designing policies to enhance the settlement intention of the NGHEMs and to improve the development of urban e-services. [ABSTRACT FROM AUTHOR]

Background: The organizational climate that fosters and supports the implementation of evidence is a key factor influencing the effective implementation of evidence-based practice (EBP). Nurses, being the largest group of medical staff, play a crucial role in EBP implementation. The perception of the climate for EBP implementation among nurses in their organizations is unknown, especially among Chinese nurses. Aims: To clarify the implementation climate of EBP among Chinese nurses and identify the factors associated with the implementation and development of evidence-based nursing practices. Methods: This study employed a descriptive cross-sectional study design. From March 2023 to April 2023, a sample of nurses (n = 1260) from two Tertiary care hospitals in central China were selected and surveyed by self-designed social-demographic questionnaire and Implementation Climate Scale. Multiple linear stepwise regression analysis was conducted to determine the predictors of implementation climate. Results: The nurses achieved a mean ICS score of 59.10 ± 11.22, with mean scores exceeding 3 points for each sub-dimension and item. In the results of multiple linear regression, income satisfaction, implementation of evidence-based nursing practice project(s) within the unit, experience of evidence-based nursing practice during working life, and specific training or courses in evidence-based nursing practice were predictors of ICS. Of these, income satisfaction was the most significant predictor. These factors could explain 17.5% of the total variance in implementation climate. Conclusion: The climate for implementing EBP in Chinese nursing organizations was relatively strong. Nursing managers can enhance the climate for implementing EBP in their organizations by actively improving salary and enhancing EBP-related trainings and practices. Relevance to clinical practice: Understanding nurses' perceptions of the EBP implementation climate in their organizations can help to identify specific barriers and facilitators to the development of EBP and facilitate its successful implementation. Patient or public contribution: Clinical nurses were involved in data collection and completed the questionnaires on EBP implementation climate. What does this paper contribute to the wider global clinical community?: The number of studies on the organization and implementation climate of evidence-based nursing practice is limited, especially in China. According to the results of this study, nurses exhibited a positive perception and recognition of the organized EBP implementation climate. Income satisfaction, implementation of evidence-based nursing practice project(s) within the unit, experience of evidence-based nursing practice during the working life and specific training or courses undertaken in evidence-based nursing practice were factors of nurses' perception of implementation climate. Organizational managers should actively provide comprehensive resource support to enhance the implementation climate of evidence-based nursing practice. [ABSTRACT FROM AUTHOR]

Background: Human communities suffered a vast socioeconomic burden in dealing with the pandemic of coronavirus disease 2019 (COVID-19) globally. Real-word data about these burdens can inform governments about evidence-based resource allocation and prioritization. The aim of this scoping review was to map the cost-of-illness (CoI) studies associated with COVID-19. Methods: This scoping review was conducted from January 2019 to December 2021. We searched cost-of-illness papers published in English within Web of Sciences, PubMed, Google Scholar, Scopus, Science Direct and ProQuest. For each eligible study, extracted data included country, publication year, study period, study design, epidemiological approach, costing method, cost type, cost identification, sensitivity analysis, estimated unit cost and national burden. All of the analyses were applied in Excel software. Results: 2352 records were found after the search strategy application, finally 28 articles met the inclusion criteria and were included in the review. Most of the studies were done in the United States, Turkey, and China. The prevalence-based approach was the most common in the studies, and most of studies also used Hospital Information System data (HIS). There were noticeable differences in the costing methods and the cost identification. The average cost of hospitalization per patient per day ranged from 101$ in Turkey to 2,364$ in the United States. Among the studies, 82.1% estimated particularly direct medical costs, 3.6% only indirect costs, and 14.3% both direct and indirect costs. Conclusion: The economic burden of COVID-19 varies from country to country. The majority of CoI studies estimated direct medical costs associated with COVID-19 and there is a paucity of evidence for direct non-medical, indirect, and intangible costs, which we recommend for future studies. To create homogeneity in CoI studies, we suggest researchers follow a conceptual framework and critical appraisal checklist of cost-of-illness (CoI) studies. [ABSTRACT FROM AUTHOR]