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Statistical models to predict clinical outcomes with anakinra vs. tocilizumab treatments for severe pneumonia in COVID19 patients
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
European Federation of Internal Medicine. Published by Elsevier B.V.
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9899775/ https://www.ncbi.nlm.nih.gov/pubmed/36797120 http://dx.doi.org/10.1016/j.ejim.2023.01.024 |
| _version_ | 1784882703905062912 |
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| author | Piconi, Stefania Pontiggia, Silvia Franzetti, Marco Branda, Francesco Tosi, Davide |
| author_facet | Piconi, Stefania Pontiggia, Silvia Franzetti, Marco Branda, Francesco Tosi, Davide |
| author_sort | Piconi, Stefania |
| collection | PubMed |
| description | |
| format | Online Article Text |
| id | pubmed-9899775 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2023 |
| publisher | European Federation of Internal Medicine. Published by Elsevier B.V. |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-98997752023-02-06 Statistical models to predict clinical outcomes with anakinra vs. tocilizumab treatments for severe pneumonia in COVID19 patients Piconi, Stefania Pontiggia, Silvia Franzetti, Marco Branda, Francesco Tosi, Davide Eur J Intern Med Letter to the Editor European Federation of Internal Medicine. Published by Elsevier B.V. 2023-06 2023-02-06 /pmc/articles/PMC9899775/ /pubmed/36797120 http://dx.doi.org/10.1016/j.ejim.2023.01.024 Text en © 2023 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
| spellingShingle | Letter to the Editor Piconi, Stefania Pontiggia, Silvia Franzetti, Marco Branda, Francesco Tosi, Davide Statistical models to predict clinical outcomes with anakinra vs. tocilizumab treatments for severe pneumonia in COVID19 patients |
| title | Statistical models to predict clinical outcomes with anakinra vs. tocilizumab treatments for severe pneumonia in COVID19 patients |
| title_full | Statistical models to predict clinical outcomes with anakinra vs. tocilizumab treatments for severe pneumonia in COVID19 patients |
| title_fullStr | Statistical models to predict clinical outcomes with anakinra vs. tocilizumab treatments for severe pneumonia in COVID19 patients |
| title_full_unstemmed | Statistical models to predict clinical outcomes with anakinra vs. tocilizumab treatments for severe pneumonia in COVID19 patients |
| title_short | Statistical models to predict clinical outcomes with anakinra vs. tocilizumab treatments for severe pneumonia in COVID19 patients |
| title_sort | statistical models to predict clinical outcomes with anakinra vs. tocilizumab treatments for severe pneumonia in covid19 patients |
| topic | Letter to the Editor |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9899775/ https://www.ncbi.nlm.nih.gov/pubmed/36797120 http://dx.doi.org/10.1016/j.ejim.2023.01.024 |
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