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Background: Sex-specific prediction models for low hemoglobin (Hb) deferral have been developed in Dutch whole blood donors. In this study, we validated and updated the models in a cohort of Swiss whole blood donors.
Methods: Prospectively collected data from 53,772 Swiss whole blood donors were used. The predictive performance of the Dutch models was assessed in terms of calibration (agreement between predicted probabilities and observed frequencies) and discrimination (ability to discriminate between deferred and approved donors). The models were updated by revising the strength of the individual predictors in the models.
Results: A total of 1,065 men (3.3%) and 2,063 women (9.7%) were deferred from donation because of a low Hb level. Validation in Swiss donors demonstrated underestimation of predicted risks and significantly lower discriminative ability. The predictive effects of most predictors were weaker in Swiss donors. Updating the models increased the calibration for both men and women, and slightly increased the discriminative ability in men.
Conclusion: Validation of the Dutch prediction models in Swiss whole blood donors showed lower, though adequate performance. In general, the Dutch prediction models can reliably predict the risk of Hb deferral, although for application in other countries small adaptations are necessary.
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http://dx.doi.org/10.1159/000446817 | DOI Listing |
Driven by eutrophication and global warming, the occurrence and frequency of harmful cyanobacteria blooms (CyanoHABs) are increasing worldwide, posing a serious threat to human health and biodiversity. Early warning enables precautional control measures of CyanoHABs within water bodies and in water works, and it becomes operational with high frequency in situ data (HFISD) of water quality and forecasting models by machine learning (ML). However, the acceptance of early warning systems by end-users relies significantly on the interpretability and generalizability of underlying models, and their operability.
View Article and Find Full Text PDFWater Res
September 2025
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China. Electronic address:
Groundwater overextraction presents persistent challenges due to strategic interdependence among decentralized users. While game-theoretic models have advanced the analysis of individual incentives and collective outcomes, most frameworks assume fully rational agents and neglect the role of cognitive and social factors. This study proposes a coupled model that integrates opinion dynamics with a differential game of groundwater extraction, capturing the interaction between institutional authority and evolving stakeholder preferences.
View Article and Find Full Text PDFAm J Emerg Med
September 2025
University of Toronto, Rotman School of Management, Canada.
Study Objective: Accurately predicting which Emergency Department (ED) patients are at high risk of leaving without being seen (LWBS) could enable targeted interventions aimed at reducing LWBS rates. Machine Learning (ML) models that dynamically update these risk predictions as patients experience more time waiting were developed and validated, in order to improve the prediction accuracy and correctly identify more patients who LWBS.
Methods: The study was deemed quality improvement by the institutional review board, and collected all patient visits to the ED of a large academic medical campus over 24 months.
JMIR Res Protoc
September 2025
University of Nevada, Las Vegas, Las Vegas, NV, United States.
Background: In-hospital cardiac arrest (IHCA) remains a public health conundrum with high morbidity and mortality rates. While early identification of high-risk patients could enable preventive interventions and improve survival, evidence on the effectiveness of current prediction methods remains inconclusive. Limited research exists on patients' prearrest pathophysiological status and predictive and prognostic factors of IHCA, highlighting the need for a comprehensive synthesis of predictive methodologies.
View Article and Find Full Text PDFPurpose: In Armenia, a lower-middle-income country, cancer causes 21% of all deaths, with over half of cases diagnosed at advanced stages. Without universal health insurance, patients rely on out-of-pocket payments or black-market channels for costly immunotherapies, underscoring the need for real-world data to inform equitable policy reforms.
Methods: We conducted a multicenter, retrospective cohort study of patients who received at least one dose of an immune checkpoint inhibitor (ICI) between January 2017 and December 2023 across six Armenian oncology centers.