PubMed COVID-19 Clinical Care

No abstract

CONCLUSIONS: The present results are useful for deciding whether to perform CT in COVID-19 patients. High-risk patients such as those mentioned above should undergo CT.

This qualitative study examined the effects of the COVID-19 pandemic on the lives of patients living with chronic pain (CP). Patients referred to an interdisciplinary pain management program between July and December of 2021 were asked to respond to

INTRODUCTION: Vaccinations are ideal for reducing the severity of clinical manifestations and secondary complications of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); however, SARS-CoV-2 continues to cause morbidity and mortality

CONCLUSIONS: Our findings suggest that a virtual platform can be a convenient and effective way to deliver values clarification workshops on abortion, and this technology could be leveraged to expand access to this training in areas without trained

The hepatitis E virus (HEV) is an emergent zoonotic virus causing viral hepatitis worldwide. Clinically, hepatitis E is not easily distinguished from other types of acute viral hepatitis. There is a need for HEV diagnostic assays to detect and

CONCLUSIONS: Patients undergoing lumbar spinal fusion within 2 weeks from initial COVID-19 diagnosis are at increased risk for perioperative venous thromboembolic events and sepsis. This effect does not persist beyond 2 weeks, however, so it may be

CONCLUSIONS: About 34% of PWE experienced exacerbated epileptic seizures after COVID-19 infection. Male patients, young patients, patients with the history of drug resistance, and the patients who had higher seizure frequency were at increased risk

CONCLUSION: Since 80% of our patients had exposure to coronavirus, a potential relationship might be suggested between the two. Furthermore, as we witnessed in one case, urgent endoscopic investigation in immunocompromised patients presenting with

The black-box nature of most artificial intelligence (AI) models encourages the development of explainability methods to engender trust into the AI decision-making process. Such methods can be broadly categorized into two main types: post hoc