Highlights Controlling age and sex, NLR>6.5 increases the chance of disease severity by 4 times. NLR>6.5 increases the chance of death about 1.8 times after age and sex adjustments. The average value of WBC among severe patients was higher than non-severe.

Background In the last few decades, the need to change the curriculum of basic medical science has been further emphasized. The purpose of this study was to evaluate the effects of teaching integrated course of physical examination and radiological anatomy in practical limb anatomy on medical students’ learning outcomes. Methods This was an experimental study. Medical students (of the 4th semester of medical education) were divided into intervention and control groups. Related topics of physical examination and radiological anatomy were added to the practical limb anatomy courses of the intervention group. Practical knowledge of anatomy, clinical applications of anatomical knowledge, students ‘satisfaction, and students’ attitude toward the anatomy course were assessed at the end of the study. Knowledge retention was assessed three months after the semester. Results The intervention group scored significantly higher mean scores in practical knowledge of anatomy test, clinical applications of anatomical knowledge test and knowledge retention test (P-value < 0.05). In evaluating students’ satisfaction with the course, the intervention group was satisfied with the course and teacher performance and had appropriate attitude (Mean˃4, Max score = 5) towards the application of anatomy in medicine. Conclusions The findings of this study showed that teaching practical anatomy with a clinical integrated approach can improve the practical knowledge of anatomy, knowledge retention, and clinical applications of anatomical knowledge. In addition, an integrated approach was associated with greater student satisfaction and it makes students have appropriate attitude towards the application of anatomy in medicine.

Like other aspects of the health care system, medical education has been greatly affected by the COVID-19 pandemic. To follow the requirements of lockdown and virtual education, the performance of students has been evaluated via web-based examinations. Although this shift to web-based examinations was inevitable, other mental, educational, and technical aspects should be considered to ensure the efficiency and accuracy of this type of evaluation in this era. The easiest way to address the new challenges is to administer traditional questions via a web-based platform. However, more factors should be accounted for when designing web-based examinations during the COVID-19 era. This article presents an approach in which the opportunity created by the pandemic is used as a basis to reconsider learning as the main goal of web-based examinations. The approach suggests using open-book examinations, using questions that require high cognitive domains, using real clinical scenarios, developing more comprehensive examination blueprints, using advanced platforms for web-based questions, and providing feedback in web-based examinations to ensure that the examinees have acquired the minimum competency levels defined in the course objectives.

The COVID-19 pandemic has now imposed an enormous global burden as well as a large mortality in a short time period. Although there is no promising treatment, identification of early predictors of in-hospital mortality would be critically important in reducing its worldwide mortality. We aimed to suggest a prediction model for in-hospital mortality of COVID-19. In this case–control study, we recruited 513 confirmed patients with COVID-19 from February 18 to March 26, 2020 from Isfahan COVID-19 registry. Based on extracted laboratory, clinical, and demographic data, we created an in-hospital mortality predictive model using gradient boosting. We also determined the diagnostic performance of the proposed model including sensitivity, specificity, and area under the curve (AUC) as well as their 95% CIs. Of 513 patients, there were 60 (11.7%) in-hospital deaths during the study period. The diagnostic values of the suggested model based on the gradient boosting method with oversampling techniques using all of the original data were specificity of 98.5% (95% CI: 96.8–99.4), sensitivity of 100% (95% CI: 94–100), negative predictive value of 100% (95% CI: 99.2–100), positive predictive value of 89.6% (95% CI: 79.7–95.7), and an AUC of 98.6%. The suggested model may be useful in making decision to patient’s hospitalization where the probability of mortality may be more obvious based on the final variable. However, moderate gaps in our knowledge of the predictors of in-hospital mortality suggest further studies aiming at predicting models for in-hospital mortality in patients with COVID-19.

Digital health as a rapidly growing medical field relies comprehensively on human health data. Conventionally, the collection of health data is mediated by officially diagnostic instruments, operated by health professionals in clinical environments and under strict regulatory conditions. Mobile health, telemedicine, and other smart devices with Internet connections are becoming the future choices for collecting patient information. Progress of technologies has facilitated smartphones, wearable devices, and miniaturized health-care devices. These devices allow the gathering of an individual's health-care information at the patient's home. The data from these devices will be huge, and by integrating such enormous data using Artificial Intelligence, more detailed phenotyping of disease and more personalized medicine will be realistic. The future of medicine will be progressively more digital, and recognizing the importance of digital technology in this field and pandemic preparedness planning has become urgent.