Helmut Schmidt

ABSTRACT Powerful business interests threaten to capture policy design. From a quiet politics perspective, businesses are especially

China is managing its government debt burden through refinancing and swaps that buy time but fail to address structural flaws, risking a prolonged balance sheet recession. Sequenced reforms should look to stabilise essentials first, broaden revenues and pool social insurance, then resolve weak entities and cleanse bank balance sheets.

Florian Wagner-von Papp is Professor of Private and Business Law at the Helmut Schmidt University/University of the Federal Armed Forces in Hamburg (HSU) with a research focus on antitrust/competition law, comparative law and contract design. He joined HSU in 2019 from University College London (UCL) where he had been Professor for Antitrust and Comparative Law & Economics (and where he had been teaching since 2005). He has degrees from the University of Tübingen (First State Examination 1998; Doctorate 2004) and Columbia Law School, New York (LL.M. 2002) as well as the full German qualification to practice law (Second State Examination 2000). Florian had visiting positions at Harvard Law School (Visiting Researcher 2011/12), the University of Würzburg (Visiting Professor December 2017; January/December 2018) and Osaka University (Special Researcher March 2018; Visiting Professor May 2018 until end of March 2019); between 2008 and 2017 he taught tutorials in competition law at the University of Oxford. In 2017/18 he joined and taught at the HSU on a temporary basis.

Abstract Background: Somatic Symptom Disorder (SSD) is characterised by a complex interplay between physiological regulation and psychological distress.

The distinctiveness of aesthetic emotions

Abstract The biological mechanisms underlying the spontaneous and recurrent transition to seizures in the epileptic brain are still poorly understood.

Diagnosis of heart failure is complex but crucial for patient outcomes and often hindered by the untapped potential of unstructured clinical notes. We introduce a novel end-to-end pipeline for heart failure diagnosis, leveraging electronic health records (EHR) and German clinical notes from 846 patients. Our pipeline synthesizes abbreviation disambiguation, translation of German clinical notes to English, medical entity linking to SNOMED-CT, and subsequent classification. The classification was performed using a Support Vector Machine (SVM) and compared against a fine-tuned medBERT.de neural baseline. We reduced the reliance on training data with zero-shot learning to address limitations with abbreviation disambiguation and entity linking approaches. Validation against benchmark datasets and cardiologists demonstrates high accuracy for real clinical use. Abbreviation disambiguation achieved an accuracy of up to 96.1%. Entity linking achieved competitive performance compared to state-of-the-art approaches on selected evaluation datasets. The SVM classification approach utilizing SNOMED-CT concepts and EHR data achieved an F1-score of 65.3%, on par with the medBERT.de neural baseline using clinical notes and EHR data. Despite challenges regarding limited language-specific resources and reference dataset availability for SNOMED-CT annotations in German, our pipeline demonstrates high potential for real-world clinical use and clinical decision support grounded in the standardized SNOMED-CT ontology.