107036 research outputs found
Sort by
5-year experience of haemophilia centre certification performed by the German, Austrian and Swiss Society for Thrombosis and Haemostasis Research [Abstract]
No full textValidation of machine learning based scenario generators
No full textMachine learning (ML) methods are becoming increasingly important for designing economic scenario generators for internal models. Validating data-driven models requires different methods than validating classical, theory-based models. We discuss two novel aspects of such validation: first, checking the multivariate distribution of risk factors, and second, detecting unwanted memorization effects. The first task is necessary because, in ML-based methods, dependencies are driven by data rather than derived from a financial-mathematical theory. To address this first issue, we propose using an existing test from the literature. The second task is necessary because it cannot be ruled out that ML-based models merely reproduce empirical data rather than generating new scenarios. For the second issue, we introduce a novel memorization ratio together with a thorough discussion. We include numerical experiments based on real market data and validate a simple autoencoder-based scenario generator
Combined [223Ra]Radiumdichloride and [177Lu]Lu-PSMA-617 therapy in end-stage metastatic castration-resistant prostate cancer
No full textEine prospektive Studie zur Untersuchung der Wirksamkeit und Sicherheit von Tiotropium bei partiellem und unkontrolliertem Asthma im Vorschulalter (Tipp) [Abstract]
No full textIst die serologische Bienen- und Wespengift-sIgE-Ratio ergänzt durch komponentenbasierte Diagnostik eine verlässliche Alternative zum Hauttest bei Hymenopterengiftallergie?
No full textAlbumin replacement therapy in septic shock: a randomized clinical trial
No full textImportance: Albumin supplementation may reduce mortality in patients with septic shock; however, data from randomized clinical trials are limited.
Objective: To assess the impact of albumin administration on outcomes in patients with septic shock.
Design, setting, and participants: This multicenter, open-label randomized clinical trial was conducted between October 21, 2019, and May 2, 2022. Patients from 23 intensive care units in Germany enrolled within 24 hours of the onset of septic shock were followed up for outcome data up to 90 days. The statistical trial report was completed and filed with the federal authorities in December 2023; additional analyses were completed in October 2024. The study was terminated prematurely due to low enrollment rates.
Interventions: Protocol group patients received 20% albumin to maintain serum albumin levels of at least 3.0 g/dL for up to 28 days during their intensive care unit admission. The control group received standard fluid administration with crystalloids.
Main outcomes and measures: The primary end point was 90-day mortality; secondary end points included 28-day, 60-day, intensive care unit and in-hospital mortality, organ dysfunction or failure, total amount of fluid administration and total fluid balance while in the intensive care unit, duration of intensive care and hospital stays, and frequency of adverse events.
Results: Of 440 randomized patients (median [IQR] age, 69 [59-78] years; 290 [65.9%] male), 222 received albumin and 218 received standard fluids. Baseline characteristics were comparable. Ninety-day mortality was 43.3% (91 of 210) in the albumin group vs 45.9% (96 of 209) in controls (relative risk, 0.94; 95% CI, 0.76-1.17; P = .71). No significant differences were observed for secondary end points.
Conclusions and relevance: In this randomized clinical trial of patients with septic shock, albumin administration was safe but did not improve 90-day survival. As this trial was prematurely terminated, results remain inconclusive and additional studies are recommended
A Copula-XAI framework for assessing compound typhoon disaster-chain risks and driving mechanisms in coastal mountainous cities: evidence from Fujian, China
No full textStudy region
Fujian Province, China.
Study focus
To address the limited quantitative understanding of compound disaster chain risks in highly urbanized mountainous coastal regions, this study develops an integrated framework combining a Copula-based joint probability model with explainable machine learning (XGBoost–SHAP). Using Fujian Province as a case study, we identify high-risk areas, quantify exposure inequality, and analyze key driving factors, nonlinear thresholds, and transition mechanisms across typical typhoon disaster chains.
New insights for the region
High-risk areas of typhoon disaster chains in Fujian Province show a clear spatial contrast, with single disaster chains being widely distributed and compound disaster chains strongly clustered. Although compound-chain high-risk areas account for only 0.8 % of the provincial area, they concentrate 12.6 % of the population and 14.1 % of economic activity. Correspondingly, population and GDP exposure lift values reach 16.3 and 18.2, respectively, which are substantially higher than those of single disaster chains, indicating pronounced exposure inequality. Overall, typhoon disaster chain risks follow a “natural triggering–social amplification” pathway and exhibit nonlinear threshold behavior. Transitions from single to compound disaster chains are governed by two dominant pathways: socioeconomic-driven and naturally driven transitions. These findings support fine-scale identification and differentiated management of compound disaster chain risks in mountainous coastal cities