Articles: sars-cov-2.
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Developing the clinical reasoning skills necessary to becoming an astute diagnostician is essential for medical students. While some medical schools offer longitudinal opportunities for students to practice clinical reasoning during the preclinical curriculum, there remains a paucity of literature fully describing what that curriculum looks like. As a result, medical educators struggle to know what an effective clinical reasoning curriculum should look like, how it should be delivered, how it should be assessed, or what faculty development is necessary to be successful. We present our Introduction to Clinical Reasoning course that is offered throughout the preclinical curriculum of the Uniformed Services University of the Health Sciences. The course introduces clinical reasoning through interactive lectures and 28 case-based small group activities over 15 months.The curriculum is grounded in script theory with a focus on diagnostic reasoning. Specific emphasis is placed on building the student's semantic competence, constructing problem lists, comparing and contrasting similar diagnoses, constructing a summary statement, and formulating a prioritized differential diagnosis the student can defend. Several complementary methods of assessment are utilized across the curriculum. These include assessments of participation, knowledge, and application. The course leverages clinical faculty, graduate medical education trainees, and senior medical students as small group facilitators. Feedback from students and faculty consistently identifies the course as a highly effective and engaging way to teach clinical reasoning. ⋯ Our Introduction to Clinical Reasoning course offers students repeated exposure to well-selected cases to promote their development of clinical reasoning. The course is an example of how clinical reasoning can be taught across the preclinical curriculum without extensive faculty training in medical education or clinical reasoning theory. The course can be adapted into different instructional formats to cover a variety of topics to provide the early learner with sequential exposure and practice in diagnostic reasoning.
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Aims/Background: The coronavirus disease 2019 (COVID-19) pandemic has highlighted the need for accurate and efficient diagnostic methods. This study aims to improve COVID-19 detection by integrating chest X-ray (CXR) and computerized tomography (CT) images using deep learning techniques, further improving diagnostic accuracy by using a combined imaging approach. Methods: The study used two publicly accessible databases, COVID-19 Questionnaires for Understanding the Exposure (COVID-QU-Ex) and Integrated Clinical and Translational Cancer Foundation (iCTCF), containing CXR and CT images, respectively. ⋯ The EfficientNet-based models, with their superior feature extraction capabilities, show better performance than ResNet models. Grad-CAM Visualizations provide insights into the model's decision-making process, potentially reducing diagnostic errors and accelerating diagnosis processes. This approach can improve patient care and support healthcare systems in managing the pandemic more effectively.
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Background: Coronavirus Disease-2019 (COVID-19) has posed formidable challenges to healthcare systems. Exploring novel biomarkers that can provide valuable prognostic insights, particularly in critically ill patients, has a significant importance. Against this backdrop, our study aims to elucidate the associations between serum chloride levels and clinical outcomes. ⋯ A total of 65 (13%) patients died, 40 (61.5%) of whom received tocilizumab; 41 (63%) were in the ICU. Serum chloride levels upon admission were markedly lower and elevated D-dimer levels were apparent in tocilizumab users, patients requiring ICU care, and patients who died. Conclusions: our findings provide robust evidence supporting the value of serum chloride levels as a prognostic biomarker in critically ill COVID-19 patients.
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Early in the SARS-CoV2 pandemic, in this journal, Hou et al. (BMC Med 18:216, 2020) interpreted public genotype data, run through functional prediction tools, as suggesting that members of particular human populations carry potentially COVID-risk-increasing variants in genes ACE2 and TMPRSS2 far more often than do members of other populations. Beyond resting on predictions rather than clinical outcomes, and focusing on variants too rare to typify population members even jointly, their claim mistook a well known artifact (that large samples reveal more of a population's variants than do small samples) as if showing real and congruent population differences for the two genes, rather than lopsided population sampling in their shared source data. We explain that artifact, and contrast it with empirical findings, now ample, that other loci shape personal COVID risks far more significantly than do ACE2 and TMPRSS2-and that variation in ACE2 and TMPRSS2 per se unlikely exacerbates any net population disparity in the effects of such more risk-informative loci.
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Although patients with myocarditis after COVID-19 mRNA vaccination appear to have a good prognosis near hospital discharge, their longer-term prognosis and management remain unknown. ⋯ Patients with post-COVID-19 mRNA vaccination myocarditis, contrary to those with post-COVID-19 myocarditis, show a lower frequency of cardiovascular complications than those with conventional myocarditis at 18 months. However, affected patients, mainly healthy young men, may require medical management up to several months after hospital discharge.