This article was migrated. The article was marked as recommended. Introduction: On March 18, 2020, in face of COVID-19 pandemic and the suspension of in-person activities by the Board of the Paulista School of Medicine (EPM), students on clinical rotations (5th and 6th grades) organized themselves to support the local community and the Hospital São Paulo complex. Method: The construction of the Volunteering-EPM was, despite fast, progressive, following as required by the Hospital São Paulo-Escola Paulista de Medicina complex. Results: After one week, Volunteering-EPM added more than 100 students and the unconditional support of professors. The quantifiable results enable an adequate supply of resources to the hospital complex. However, the biggest impact was the moment of solidarity promoted by the initiative. Discussion: Volunteering enabled unique experiences for those involved, enhancing students and professor's skill sets otherwise not developed in medical school. Emphasizing the humanitarian view of medicine improved employee and community health access and welfare. Conclusion: The speed with which actions were implemented and their impact on the community shows the ability for transformation of the volunteers. The immediate demands have been solved. In medium and long term, the project continues to respond to the new demands of the hospital.

A machine learning approach is a useful tool for risk-stratifying patients with respiratory symptoms during the COVID-19 pandemic, as it is still evolving. We aimed to verify the predictive capacity of a gradient boosting decision trees (XGboost) algorithm to select the most important predictors including clinical and demographic parameters in patients who sought medical support due to respiratory signs and symptoms (RAPID RISK COVID-19). A total of 7336 patients were enrolled in the study, including 6596 patients that did not require hospitalization and 740 that required hospitalization. We identified that patients with respiratory signs and symptoms, in particular, lower oxyhemoglobin saturation by pulse oximetry (SpO2) and higher respiratory rate, fever, higher heart rate, and lower levels of blood pressure, associated with age, male sex, and the underlying conditions of diabetes mellitus and hypertension, required hospitalization more often. The predictive model yielded a ROC curve with an area under the curve (AUC) of 0.9181 (95% CI, 0.9001 to 0.9361). In conclusion, our model had a high discriminatory value which enabled the identification of a clinical and demographic profile predictive, preventive, and personalized of COVID-19 severity symptoms.