Publications by authors named "C So-Osman"
Introduction: When Coronavirus Disease-19 (COVID-19) struck the world in December 2019, initiatives started to investigate the efficacy of convalescent plasma, a readily available source of passive antibodies, collected from recovered patients as a therapeutic option. This was based on historical observational data from previous virus outbreaks.
Methods: A scoping review was conducted on the efficacy and safety of convalescent plasma and hyperimmune immunoglobulins for COVID-19 treatment.
Artificial intelligence (AI) uses sophisticated algorithms to "learn" from large volumes of data. This could be used to optimise recruitment of blood donors through predictive modelling of future blood supply, based on previous donation and transfusion demand. We sought to assess utilisation of predictive modelling and AI blood establishments (BE) and conducted predictive modelling to illustrate its use.
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- * A mixed-method approach was used, combining surveys and interviews from participants across 13 institutions, revealing various funding sources and diverse assessment methods for measuring learner outcomes.
- * Key facilitators include management support and compliance audits, while barriers involve challenges like time allocation for development, content updates, and access to performance data; there's a call for more research on the impact of these e-learning programs on actual transfusion practices.
Background: Immunoglobulin (IG) therapy is widely used to treat primary and secondary immune deficiencies and as immunomodulatory agent for various disorders. There is great concern that shortages of IG may rise, potentially affecting medical treatment options.
Study Design And Methods: An international survey was developed to study how intravenous immunoglobulins (IVIGs) are used and managed within hospitals in case of shortages.