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Rezension: Horst G. Herrmann, Im Moralapostolat. Die Geburt der westlichen Moral aus dem Geist der Reformation. Edition Sonderwege, Berlin: Manuscriptum Verlagsbuchhandlung, 2. Aufl. 2018
No full textRezension: Peter Hoeres/Anuschka Tischer (Hrsg.), Medien der Außenbeziehungen von der Antike bis zur Gegenwart, Köln: Böhlau 2017
No full textAdverse events after metastases-directed stereotactic radiotherapy and biological cancer therapy
No full textMetastases-directed stereotactic radiotherapy (SRT) is increasingly performed in patients with metastatic or oligometastatic cancer treated with immune checkpoint inhibitors (ICIs), monoclonal antibodies (mAbs), and small-molecule drugs (SMs). However, little is known about potential interactions between SRT and biological cancer therapy (BCT).
To prospectively investigate adverse events associated with SRT combined with concurrent BCT.
This international, prospective, multicenter, noninterventional registry cohort study (Toxicity and Efficacy of Combined Stereotactic Radiotherapy and Systemic Targeted or Immune Therapy [TOaSTT]) was conducted between July 2017 and August 2019 with a 24-month follow-up. Patients from 27 centers whose cancer was treated with metastases-directed SRT concurrently with BCT were eligible. Analyses were performed in January 2025.
Patients treated with SRT for intracranial or extracranial metastases and concurrent (within ≤30 days) BCT. Indication for treatment, decision on the radiotherapy dose and fractionation, as well as interruption of BCT, were left to the discretion of the treating clinician.
Outcomes and Measures The primary outcome was severe (at least grade 3) adverse events of combined modality treatment, as graded by the treating physician. Overall survival (OS) and progression-free survival (PFS) were secondary end points
Predicting nursing staff care capacities in hospitals with machine learning
No full textBackground: Hospitals struggle to find adequate staff to secure an optimal quality of care. Continuing nursing staff shortages combined with absences for scheduled personnel, extended by high workloads, lead to complex shift planning problems in hospitals. The limited staff available must be used as efficiently as possible. We are developing methods to predict the available nursing staff care capacity and the absences of staff for a hospital ward, predictions that allow for better planning. This leads us to a prediction of the realized and missed care capacity for up to 14 days.
Methods: We propose predictions based on state-of-the-art machine learning approaches (Deep Neural Network and Extreme Gradient Boosting) and evaluate them based on actual datasets from a university hospital. We use data extracted from past schedules and calendar variables as inputs for our forecasts. A large part of this information remains an untapped potential for optimization. However, data is still scarce. Thus, we investigate whether incorporating information from a different ward can enhance prediction as well. We implemented this data enrichment process in two ways: once by including data from other wards as additional features, and once by adding the information as extra data points.
Results: Our study demonstrates that both missed and realized nursing care capacities can be reliably predicted using machine learning. Incorporating data from other wards might improve prediction accuracy for certain forecast horizons. Our Machine Learning approaches outperformed classical time series forecasts based on Holt-Winters, reducing the mean squared error (MSE) of missed and realized care capacity predictions by up to 33.9% and 43.6%, and reducing the mean absolute error (MAE) by up to 18.3% and 24.6%, respectively. Compared to conventional planning, which is typically based on a moving average approach, the models reduce MSE by up to 23.8% and 46.8% and MAE by up to 10.6% and 25.8%, respectively.
Conclusions: These predictions provide valuable decision support for shift scheduling by leveraging historical staffing and care data, allowing hospitals to define target staff levels and appropriate safety buffers. Overall, our study makes an important contribution to improving workforce planning in hospitals facing limited nursing capacities
Smart villagers and digital social innovation: the transformation of rural communities in the age of digital mediatisation
No full textThis thesis summarises, consolidates, and completes a cumulative dissertation project located at the interface of mediatisation and innovation in rural areas. Focussing on six German villages as case studies, it explores the emergence and impact of Digital Social Innovation (DSI) in the context of rural development. Hereby it addresses a significant gap in understanding how DSI processes reshape rural social practices, challenging the urban bias in innovation research. It examines how rural communities are engaging with DSI and actively responding to local challenges such as declining infrastructure, demographic change, and social fragmentation. Methodically a mixed-method approach, based on a focused ethnography, is employed. Insights are drawn from expert interviews, digital-biographical interviews, digitalisation-historical interviews, participatory observations, document analysis, and app user data analysis. Therefore the data provides a comprehensive perspective on the complex entan-glement between digital technologies and rural social structures. The theoretical framework brings together strands of literature from innovation and social innovation studies, digital mediatisation theory and the communicative figurations approach. Such an interdisciplinary symmetry enables an overall examination of the rural DSI processes.
Key findings introduce the concept of ‘Smart Villagers’ as agents of DSI in rural areas, thereby demon-strating that innovation is not solely an urban phenomenon. The research reveals a ‘click-by-click’ pro-cess of rural community development, emphasising the phases and dynamics involved in developing and establishing DSI in rural areas. The approach of communicative figurations is tested and used to investigate the transformational processes of rural mediatisation. The results show that the implemen-tation of a village-specific communication app is linked to a transformation of power dynamics and the sense of belonging to the village community.
In addition to its scholarly contributions, the dissertation provides significant insights for policymakers and practitioners involved in rural development initiatives. By illustrating the dynamics of DSI in rural settings, it unveils and fosters novel approaches to confronting rural challenges, and therefore pro-moting a more diverse perspective on innovation across different spatial contexts
