Excessive self-medication with over-the-counter drugs is an issue commonly encountered by health care professionals. It can result in uncommon presentations of life-threatening illnesses. These medications are frequently overlooked by clinicians when taking histories from patients, and their risks are often downplayed. We present the case of a 35-year-old woman with acute pancreatitis secondary to hypercalcemia. This condition occurred due to long-term excessive self-administration of calcium-rich antacid tablets. Her clinical course involved multifactorial rebound hypocalcemia after treatment and multiple complications from the abuse of other non-prescription medications. Acute pancreatitis secondary to antacid-induced milk-alkali syndrome has been minimally reported in the literature. There are no reports describing rebound hypocalcemia as a complication of its treatment or presenting this pathology in the context of multiple over-the-counter drug abuse. This case highlights the importance of taking thorough drug histories, including non-prescription medications, in acute clinical assessments.
We present a weighted estimator of the covariance and correlation in bipartite complex systems with a double layer of heterogeneity. The advantage provided by the weighted estimators lies in the fact that the unweighted sample covariance and correlation can be shown to possess a bias. Indeed, such a bias affects real bipartite systems, and, for example, we report its effects on two empirical systems, one social and the other biological. On the contrary, our newly proposed weighted estimators remove the bias and are better suited to describe such systems.
Fraud is a social phenomenon, and fraudsters often collaborate with other fraudsters, taking on different roles. The challenge for insurance companies is to implement claim assessment and improve fraud detection accuracy. We developed an investigative system based on bipartite networks, highlighting the relationships between subjects and accidents or vehicles and accidents. We formalize filtering rules through probability models and test specific methods to assess the existence of communities in extensive networks and propose new alert metrics for suspicious structures. We apply the methodology to a real database—the Italian Antifraud Integrated Archive—and compare the results to out‐of‐sample fraud scams under investigation by the judicial authorities.
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