{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["10(3)"],"submitter":["Gonzalez-Escamilla M"],"pubmed_abstract":["An early detection tool for latent COVID-19 infections in oncology staff and patients is essential to prevent outbreaks in a cancer center. (1) Background: In this study, we developed and implemented two early detection tools for the radiotherapy area to identify COVID-19 cases opportunely. (2) Methods: Staff and patients answered a questionnaire (electronic and paper surveys, respectively) with clinical and epidemiological information. The data were collected through two online survey tools: Real-Time Tracking (R-Track) and Summary of Factors (S-Facts). Cut-off values were established according to the algorithm models. SARS-CoV-2 qRT-PCR tests confirmed the positive algorithms individuals. (3) Results: Oncology staff members (n = 142) were tested, and 14% (n = 20) were positives for the R"],"journal":["Healthcare (Basel, Switzerland)"],"pagination":["462"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8950794"],"repository":["biostudies-literature"],"pubmed_title":["Epidemiological Algorithm for Early Detection of COVID-19 Cases in a Mexican Oncologic Center."],"pmcid":["PMC8950794"],"pubmed_authors":["Rodriguez-Gutierrez HF","Vidal-Gutierrez O","Pineiro-Retif R","Garza-Rodriguez ML","Gonzalez-Escamilla M","Ortiz-Murillo VN","Alcorta-Nunez F","Perez-Ibave DC","Ramirez-Correa GA","Gonzalez-Guerrero JF","Burciaga-Flores CH","Rodriguez-Nino P"],"additional_accession":[]},"is_claimable":false,"name":"Epidemiological Algorithm for Early Detection of COVID-19 Cases in a Mexican Oncologic Center.","description":"An early detection tool for latent COVID-19 infections in oncology staff and patients is essential to prevent outbreaks in a cancer center. (1) Background: In this study, we developed and implemented two early detection tools for the radiotherapy area to identify COVID-19 cases opportunely. (2) Methods: Staff and patients answered a questionnaire (electronic and paper surveys, respectively) with clinical and epidemiological information. The data were collected through two online survey tools: Real-Time Tracking (R-Track) and Summary of Factors (S-Facts). Cut-off values were established according to the algorithm models. SARS-CoV-2 qRT-PCR tests confirmed the positive algorithms individuals. (3) Results: Oncology staff members (n = 142) were tested, and 14% (n = 20) were positives for the R","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Mar","modification":"2025-04-21T15:39:43.783Z","creation":"2025-04-21T15:39:43.783Z"},"accession":"S-EPMC8950794","cross_references":{"pubmed":["35326940"],"doi":["10.3390/healthcare10030462"]}}