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Chronic related group classification system as a new public health tool to predict risk and outcome of COVID-19 in patients with systemic rheumatic diseases: A population-based study of more than forty thousand patients
| Autores principales: | , , , , , , , , , , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Société française de rhumatologie. Published by Elsevier Masson SAS.
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9677569/ https://www.ncbi.nlm.nih.gov/pubmed/36423782 http://dx.doi.org/10.1016/j.jbspin.2022.105497 |
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| author | De Lorenzis, Enrico Parente, Paolo Natalello, Gerlando Soldati, Salvatore Bosello, Silvia Laura Barbara, Andrea Sorge, Chiara Axelrod, Svetlana Verardi, Lucrezia Cerasuolo, Pier Giacomo Peluso, Giusy Gemma, Antonella Davoli, Marina Biliotti, Donatella Bruzzese, Vincenzo Goletti, Mauro Di Martino, Mirko D’Agostino, Maria Antonietta |
| author_facet | De Lorenzis, Enrico Parente, Paolo Natalello, Gerlando Soldati, Salvatore Bosello, Silvia Laura Barbara, Andrea Sorge, Chiara Axelrod, Svetlana Verardi, Lucrezia Cerasuolo, Pier Giacomo Peluso, Giusy Gemma, Antonella Davoli, Marina Biliotti, Donatella Bruzzese, Vincenzo Goletti, Mauro Di Martino, Mirko D’Agostino, Maria Antonietta |
| author_sort | De Lorenzis, Enrico |
| collection | PubMed |
| description | |
| format | Online Article Text |
| id | pubmed-9677569 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2023 |
| publisher | Société française de rhumatologie. Published by Elsevier Masson SAS. |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-96775692022-11-21 Chronic related group classification system as a new public health tool to predict risk and outcome of COVID-19 in patients with systemic rheumatic diseases: A population-based study of more than forty thousand patients De Lorenzis, Enrico Parente, Paolo Natalello, Gerlando Soldati, Salvatore Bosello, Silvia Laura Barbara, Andrea Sorge, Chiara Axelrod, Svetlana Verardi, Lucrezia Cerasuolo, Pier Giacomo Peluso, Giusy Gemma, Antonella Davoli, Marina Biliotti, Donatella Bruzzese, Vincenzo Goletti, Mauro Di Martino, Mirko D’Agostino, Maria Antonietta Joint Bone Spine Letter to the Editor Société française de rhumatologie. Published by Elsevier Masson SAS. 2023-03 2022-11-21 /pmc/articles/PMC9677569/ /pubmed/36423782 http://dx.doi.org/10.1016/j.jbspin.2022.105497 Text en © 2022 Société française de rhumatologie. Published by Elsevier Masson SAS. 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 De Lorenzis, Enrico Parente, Paolo Natalello, Gerlando Soldati, Salvatore Bosello, Silvia Laura Barbara, Andrea Sorge, Chiara Axelrod, Svetlana Verardi, Lucrezia Cerasuolo, Pier Giacomo Peluso, Giusy Gemma, Antonella Davoli, Marina Biliotti, Donatella Bruzzese, Vincenzo Goletti, Mauro Di Martino, Mirko D’Agostino, Maria Antonietta Chronic related group classification system as a new public health tool to predict risk and outcome of COVID-19 in patients with systemic rheumatic diseases: A population-based study of more than forty thousand patients |
| title | Chronic related group classification system as a new public health tool to predict risk and outcome of COVID-19 in patients with systemic rheumatic diseases: A population-based study of more than forty thousand patients |
| title_full | Chronic related group classification system as a new public health tool to predict risk and outcome of COVID-19 in patients with systemic rheumatic diseases: A population-based study of more than forty thousand patients |
| title_fullStr | Chronic related group classification system as a new public health tool to predict risk and outcome of COVID-19 in patients with systemic rheumatic diseases: A population-based study of more than forty thousand patients |
| title_full_unstemmed | Chronic related group classification system as a new public health tool to predict risk and outcome of COVID-19 in patients with systemic rheumatic diseases: A population-based study of more than forty thousand patients |
| title_short | Chronic related group classification system as a new public health tool to predict risk and outcome of COVID-19 in patients with systemic rheumatic diseases: A population-based study of more than forty thousand patients |
| title_sort | chronic related group classification system as a new public health tool to predict risk and outcome of covid-19 in patients with systemic rheumatic diseases: a population-based study of more than forty thousand patients |
| topic | Letter to the Editor |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9677569/ https://www.ncbi.nlm.nih.gov/pubmed/36423782 http://dx.doi.org/10.1016/j.jbspin.2022.105497 |
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