In Situ Pulmonary Arterial Thrombosis: Literature Review and Clinical Significance of a Distinct Entity.

Filling defects identified in the pulmonary arterial tree are commonly presumed to represent an embolic phenomenon originating from thrombi formed in remote veins, particularly lower-extremity deep venous thrombosis (DVT). However, accumulating evidence supports an underappreciated cause for pulmonary arterial thrombosis (PAT), namely, de novo thrombogenesis-whereby thrombosis arises within the pulmonary arteries in the absence of DVT. Although historically underrecognized, in situ PAT has become of heightened importance with the emergence of SARS-CoV-2 infection. In situ PAT is attributed to endothelial dysfunction, systemic inflammation, and acute lung injury and has been described in a range of conditions including COVID-19, trauma, acute chest syndrome in sickle cell disease, pulmonary infections, and severe pulmonary arterial hypertension. The distinction between pulmonary embolism and in situ PAT may have important implications regarding management decisions and clinical outcomes. In this review, we summarize the pathophysiology, imaging appearances, and management of in situ PAT in various clinical situations. This understanding will promote optimal tailored treatment strategies for this increasingly recognized entity.

Mandating Limits on Workload, Duty, and Speed in Radiology.

Research has not yet quantified the effects of workload or duty hours on the accuracy of radiologists. With the exception of a brief reduction in imaging studies during the 2020 peak of the COVID-19 pandemic, the workload of radiologists in the United States has seen relentless growth in recent years. One concern is that this increased demand could lead to reduced accuracy. Behavioral studies in species ranging from insects to humans have shown that decision speed is inversely correlated to decision accuracy. A potential solution is to institute workload and duty limits to optimize radiologist performance and patient safety. The concern, however, is that any prescribed mandated limits would be arbitrary and thus no more advantageous than allowing radiologists to self-regulate. Specific studies have been proposed to determine whether limits reduce error, and if so, to provide a principled basis for such limits. This could determine the precise susceptibility of individual radiologists to medical error as a function of speed during image viewing, the maximum number of studies that could be read during a work shift, and the appropriate shift duration as a function of time of day. Before principled recommendations for restrictions are made, however, it is important to understand how radiologists function both optimally and at the margins of adequate performance. This study examines the relationship between interpretation speed and error rates in radiology, the potential influence of artificial intelligence on reading speed and error rates, and the possible outcomes of imposed limits on both caseload and duty hours. This review concludes that the scientific evidence needed to make meaningful rules is lacking and notes that regulating workloads without scientific principles can be more harmful than not regulating at all.

Testing alternative models and predictive utility of the Death Anxiety Inventory-Revised: A COVID-19 related longitudinal population based study.

The beginning of the COVID-19 pandemic had a profound effect on all aspects of daily life and triggered a swell of anxiety across the world. Some suggest this emotional response to the pandemic can be explained through death anxiety (DA), a transdiagnostic dimension associated with numerous psychological disorders. However, it remains unclear as to whether DA is a unidimensional or multidimensional construct. The primary aim of this study was to examine the underlying structure of the Death Anxiety Inventory-Revised (DAI-R; Tomás-Sábado et al., 2005) and assess its associations with mental health and demographic variables during the COVID-19 pandemic. To achieve these aims, we utilized data from Waves 1 (N = 2205: collected between March 23 and March 28, 2020) and 2 (N = 1406: collected between April 22 and May 1, 2020) of the COVID-19 Psychological Research Consortium (C19PRC), a multi-wave nationally representative study. Results showed that a 4-factor model provided the best fit to the data compared to a unidimensional and 4-factor second-order model. Further analyses showed that DA at Wave 1 was positively associated with somatic symptoms, paranoia, depression, anxiety, and traumatic stress symptoms at Wave 2, supporting previous research that suggests that the fear of death is predictive of psychopathology. Significantly, the factor labelled 'Thoughts about Death' at Wave 1 was the strongest predictor of the five main psychological variables at Wave 2, after statistically controlling for the other latent variables. These findings highlight the transdiagnostic nature of DA and support this important diagnostic construct becoming a measure of mental health more generally within the population. It is hoped that this research will shine a light on those suffering from DA and become a catalyst for increased therapeutic intervention, funding, and research in this area.