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Sharing science to find answers

Find RECOVER Publications

Researchers within the RECOVER Initiative share their progress to understand, treat, and prevent Long COVID through research publications. Follow the latest science from RECOVER’s research studies below.

Visit the Research Summaries page to learn about RECOVER’s Long COVID research in a format that’s easy to understand.

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131 Results

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131 Results

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EHR Pediatric

A natural language processing pipeline for identifying pediatric long COVID symptoms and functional impacts in freeform clinical notes: A RECOVER study

Bunnell, HT; Reedy, C; Lorman, V; et al., JAMIA Open,
EHR Adult Broad Symptoms Risk Factors

Effect of Paxlovid treatment during acute COVID-19 on Long COVID onset: An EHR-based target trial emulation from the N3C and RECOVER Consortia

Preiss, A; Bhatia, A; Aragon, LV; et al., PLOS Medicine,
Observational Adult

Wearable-derived sleep measurements are associated with Long-COVID in the RECOVER adult cohort

Parthasarathy, S; Brosnahan, S; Sieberts, S; et al., Research Square,
Preprint
Observational Adult Broad Symptoms Risk Factors

Age-related changes in the clinical picture of Long COVID

Fain, MJ; Horne, BD; Horwitz, LI; et al., Journal of the American Geriatrics Society,
EHR Adult New-onset and Pre-existing Conditions Risk Factors

The association of Long COVID and CKD: Findings from the National Clinical Cohort Collaborative (N3C)

Anzalone, AJ; Krichevsky, S; Yoo, YJ; et al., Clinical Journal of the American Society of Nephrology,
EHR Pediatric New-onset and Pre-existing Conditions

Respiratory and other infections following COVID

Allen, AJ; Nguyen, N; Lorman, V; et al., Pediatrics,
Pathobiology Adult

Persistent immune dysregulation during post-acute sequelae of COVID-19 is manifested in antibodies targeting envelope and nucleocapsid proteins

Kwissa, M; Mathayan, M; Salunkhe, SS; et al., bioRxiv,
Preprint
EHR Pediatric

Extracting post-acute sequelae of SARS-CoV-2 infection symptoms from clinical notes via hybrid natural language processing

Bai, Z; Xu, Z; Sun, C; et al., npj Health Systems,
EHR Adult

Re-engineering a machine learning phenotype to adapt to the changing COVID-19 landscape: A machine learning modelling study from the N3C and RECOVER consortia

Crosskey, M; McIntee, T; Preiss, S; et al., The Lancet Digital Health,
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