English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Post-acute sequelae of SARS-CoV-2 (PASC) is an increasingly recognized yetincompletely understood public health concern. Several studies have examinedvarious ways to phenotype PASC to better characterize this heterogeneouscondition. However, many gaps in PASC phenotyping research exist, including alack of the following: 1) standardized definitions for PASC based onsymptomatology; 2) generalizable and reproducible phenotyping heuristics andmeta-heuristics; and 3) phenotypes based on both COVID-19 severity and symptomduration. In this study, we defined computable phenotypes (or heuristics) andmeta-heuristics for PASC phenotypes based on COVID-19 severity and symptomduration. We also developed a symptom profile for PASC based on a common datastandard. We identified four phenotypes based on COVID-19 severity (mild vs.moderate/severe) and duration of PASC symptoms (subacute vs. chronic). Thesymptoms groups with the highest frequency among phenotypes were cardiovascularand neuropsychiatric with each phenotype characterized by a different set ofsymptoms.

Computable Phenotypes for Post-acute sequelae of SARS-CoV-2: A National COVID Cohort Collaborative Analysis