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Background: While inter-hospital transfers for patients who have suffered major trauma have been well investigated, patient flows for other injured patients, or cases with orthopedic complications, are rarely described. This study aims to analyze the affected collective and to show possible reasons, patterns, and pitfalls to optimize the process in future. Materials and methods: In a prospective cohort study, all consecutive transfers to a Level I trauma center in Germany were documented and assessed. Patients suffering a major trauma were excluded. Data on the primary treating hospital, patient characteristics, and differences between emergency and elective surgery were analyzed. Results: A total of 227 patients were included; 162 were injured, while 65 had suffered a complication after elective orthopedic surgery or had a complex orthopedic pathology. The most common diagnoses leading to transfer were pathologies of the extremities (n = 62), pathologies of the spine (n = 50), and infections (n = 18). The main reasons stated by the transferring hospitals were a lack of expertise (137 cases) and a lack of capacity (43 cases). There was a significantly higher rate of transfers due to trauma (n = 162) than for orthopedic patients (n = 65), p < 0.0001. Conclusion: There is currently no structured procedure or algorithm for transferring patients in orthopedics and trauma surgery. [ABSTRACT FROM AUTHOR]

In recent years, the application of digital technologies for learning purposes is increasingly discussed as smartphones have become an integral part of students' everyday life. These technologies are particularly promising in the so-called "transition-in" phase of the student lifecycle when first-year students start to develop a student identity and integrate into the university environment. At that stage, most premature dropouts are observed, presumably due to a lack of self-organization or self-responsibility. Considering this, a mobile app to tackle insufficient student experiences, support learning strategies, and foster self-organization in the "transition-in" phase was developed. The research at hand proposes a generalizable success model for mobile apps with a focus on first-year students, which is based on the IS success model (Delone and McLean in Inf Syst Res 3(1):60–95, 1992) and analyzes those factors that influence student satisfaction with such an app, the intention to reuse the app, and—foremost—students' learning effectiveness. The results indicate that learning effectiveness is determined both by the perceived user satisfaction and users' intention to reuse, which are particularly influenced by perceived enjoyment but also system and information quality. Finally, design principles are derived to develop similar mobile solutions. [ABSTRACT FROM AUTHOR]

We compare the performance of the linear regression model, which is the current standard in science and practice for cross-sectional stock return forecasting, with that of machine learning methods, i.e., penalized linear models, support vector regression, random forests, gradient boosted trees and neural networks. Our analysis is based on monthly data on nearly 12,000 individual stocks from 16 European economies over almost 30 years from 1990 to 2019. We find that the prediction of stock returns can be decisively improved through machine learning methods. The outperformance of individual (combined) machine learning models over the benchmark model is approximately 0.6% (0.7%) per month for the full cross-section of stocks. Furthermore, we find no model breakdowns, which suggests that investors do not incur additional risk from using machine learning methods compared to the traditional benchmark approach. Additionally, the superior performance of machine learning models is not due to substantially higher portfolio turnover. Further analyses suggest that machine learning models generate their added value particularly in bear markets when the average investor tends to lose money. Our results indicate that future research and practice should make more intensive use of machine learning techniques with respect to stock return prediction. [ABSTRACT FROM AUTHOR]

While the discussion of changes in financial accounting properties over time is already well-established, there is a lack of evidence whether changing firm compositions in empirical samples might bias cost stickiness research. We document that with each additional listing cohort, the U.S. public firm universe becomes more knowledge-intensive and, at the same time, more cost sticky. Higher reliance on temporary labor by newer listing cohorts partly mitigates this development. Our results call for the use of listing cohort-specific slopes to allow for cohort-specific estimates of cost stickiness in future research. [ABSTRACT FROM AUTHOR]