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Background: Since the consensus of ISAPP on the definition of the term "postbiotic" there has been an enthusiasm for publications in review form—their number being disproportionate to the primary research. The aim of this bibliometry is to analyze the bibliometric trends of this newfound interest in the field. Methods: Search of the PubMed database for review articles on postbiotics, published between November 2021 and June 2024. Results: Analysis was performed on 92 review articles, the number corresponding to 2.9 reviews per month. China, Poland, Italy, Iran and India had the maximum productivity among the 32 countries involved; 21 articles were published in 13 journals with the highest impact factor, while 45 were in 16 journals with an IF between 4.0 and 4.9. The authors were mainly affiliated to universities with specialization in both basic research and technology, as well as food science. The top five publications regarding the citations received, published in Foods (2), EBioMedicine, Biomolecules, and Front. Nutr., have collected between 138 and 109 citations. Conclusions: The ever-growing number of reviews regarding postbiotics is perhaps disproportionate to the actual original research in the field. Further clinical trials would extend and deepen the subject and facilitate the drowning of more robust conclusions in relation to their effects. [ABSTRACT FROM AUTHOR]

The overwhelming volume of patients in emergency departments (EDs) is a significant problem that hinders the delivery of high quality healthcare. Despite their great value, triage protocols are challenging to put into practice. This paper examines the utility of prediction models as a tool for clinical decision support, with a focus on medium-severity patients as defined by the ESI algorithm. 689 cases of medium-risk patients were gathered from the AHEPA hospital, evaluated, and their data fed three classifiers: XGBoost (XGB), Random Forest (RF) and Logistic Regression (LR), with the prediction goal being the outcome of their visit, i.e. admission or discharge. Essential features for the prediction task were determined using feature importance and distribution analysis. Despite having many missing values or high sparsity datasets, several symptoms and metrics are recommended as crucial for outcome prediction. When fed the patients’ vital signs, XGB achieved an accuracy score of 91.30%. Several chief complaints were also proven beneficial. Prediction models can, in general, not only lessen the drawbacks of triage implementation, but also enhance its delivery. [ABSTRACT FROM AUTHOR]