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Depression is a prevalent and debilitating mental health condition that poses significant challenges for healthcare providers, researchers, and policymakers. The diagnostic coding specificity of depression is crucial for improving patient care, resource allocation, and health outcomes. We propose a novel approach to assess risk-adjusted coding specificity for individuals diagnosed with depression using a vast cohort of over one million inpatient hospitalizations in the United States. Considering various clinical, demographic, and socioeconomic characteristics, we develop a risk-adjusted model that assesses diagnostic coding specificity. Results demonstrate that risk-adjustment is necessary and useful to explain variability in the coding specificity of principal (AUC = 0.76) and secondary (AUC = 0.69) diagnoses. Our approach combines a multivariate logistic regression at the patient hospitalization level to extract risk-adjusted probabilities of specificity with a Poisson Binomial approach at the facility level. This method can be used to identify healthcare facilities that over- and under-specify diagnostic coding when compared to peer-defined standards of practice.
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There is an increasing interest in sensing applications for a variety of analytes in aqueous environments, as conventional methods do not work reliably under humid conditions or they require complex equipment with experienced operators. Hydrogel sensors are easy to fabricate, are incredibly sensitive, and have broad dynamic ranges. Experiments on their robustness, reliability, and reusability have indicated the possible long-term applications of these systems in a variety of fields, including disease diagnosis, detection of pharmaceuticals, and in environmental testing. It is possible to produce hydrogels, which, upon sensing a specific analyte, can adsorb it onto their 3D-structure and can therefore be used to remove them from a given environment. High specificity can be obtained by using molecularly imprinted polymers. Typical detection principles involve optical methods including fluorescence and chemiluminescence, and volume changes in colloidal photonic crystals, as well as electrochemical methods. Here, we explore the current research utilizing hydrogel-based sensors in three main areas: (1) biomedical applications, (2) for detecting and quantifying pharmaceuticals of interest, and (3) detecting and quantifying environmental contaminants in aqueous environments.
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Previous investigations into metonymy comprehension in ASD have confounded metonymy with anaphora, and outcome with process. Here we show how these confounds may be avoided, using data from non-diagnosed participants classified using Autism Quotient. Participants read sentences containing target words with novel or established metonymic senses (e.g., Finland, Vietnam) in literal- or figurative-supporting contexts. Participants took longer to read target words in figurative contexts, especially where the metonymic sense was novel. Importantly, participants with higher AQs took longer still to read novel metonyms. This suggests a focus for further exploration, in terms of potential differences between individuals diagnosed with ASD and their neurotypical counterparts, and more generally in terms of the processes by which comprehension is achieved.