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Radionuclide imaging plays a critical role in the diagnosis and management of kidney obstruction. However, most practicing radiologists in US hospitals have insufficient time and resources to acquire training and experience needed to interpret radionuclide images, leading to increased diagnostic errors. To tackle this problem, Emory University embarked on a study that aims to develop a computer-assisted diagnostic (CAD) tool for kidney obstruction by mining and analyzing patient data comprised of renogram curves, ordinal expert ratings on the obstruction status, pharmacokinetic variables, and demographic information. The major challenges here are the heterogeneity in data modes and the lack of gold standard for determining kidney obstruction. In this article, we develop a statistically principled CAD tool based on an integrative latent class model that leverages heterogeneous data modalities available for each patient to provide accurate prediction of kidney obstruction. Our integrative model consists of three sub-models (multilevel functional latent factor regression model, probit scalar-on-function regression model, and Gaussian mixture model), each of which is tailored to the specific data mode and depends on the unknown obstruction status (latent class). An efficient MCMC algorithm is developed to train the model and predict kidney obstruction with associated uncertainty. Extensive simulations are conducted to evaluate the performance of the proposed method. An application to an Emory renal study demonstrates the usefulness of our model as a CAD tool for kidney obstruction. [ABSTRACT FROM AUTHOR]

Angler tag‐return studies are a cornerstone of fisheries research, providing insights into individual movements and estimates of exploitation, among many other applications. However, the data generated from these studies is dependent upon effective communication between anglers and scientists. As technological advances are adopted by anglers, little research has been directed at the potential benefits of incorporating modern tag reporting methods. We tagged stream‐dwelling black bass Micropterus spp. and provided anglers a choice of reporting methods (telephone, email, iNaturalist app, or a "mixed‐mode" combination thereof). Our objectives were to examine the fate of reported fish, quantify trends in data quality across reporting methods, and explore how geographic location and angler avidity may influence use of reporting methods. Ninety‐four percent of tag reports involved the release of the fish with the tag still intact, creating opportunities for longer‐term data collection. Telephone was the most commonly used reporting method; however, this method had significantly lower completeness scores (e.g., lack of photographs or specifying fate of fish) and less precise location information than other methods. In contrast, iNaturalist had the highest completeness and most precise location information but was seldom used and had increased lag times in reporting. We found no significant differences in the proportion of reporting methods used across stream locations in our study, and avid anglers appeared to be individualistic in their choice of method. Overall, our study suggests that the adoption of modern reporting methods, like email and smartphone apps, could benefit data collection efforts of angler tag‐return studies. Fisheries scientists may wish to consider which reporting methods align with their specific study objectives and with the angling public of a given study area. [ABSTRACT FROM AUTHOR]