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Generalizable Long COVID Subtypes: Findings from the NIH N3C and RECOVER Programs

medRxiv(2022)10.1101/2022.05.24.22275398Source: DataRank Database

Generalizable Long COVID Subtypes: Findings from the NIH N3C and RECOVER Programs is a research paper published in medRxiv (2022). On theSindex it has a DataRank of 1.5. It has been cited 32 times, with 26 citing works in its 1-hop citation network.

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1.5DataRank · unranked

Scored on demand from live citation data

Open Access32 citations · 26 citing works in its network

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (76 grants)OA: greenTopics (19)Linked Datasets (2)DataCite Reuse (2)IDs (PubMed, OpenAlex)Annotations (1)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (2/2)

Has DOI
Indexed in repositories

Accessible (1/2)

Open Access

Interoperable (2/2)

DataCite relations
Linked datasets

Reusable (0/3)

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DataRank Breakdown

Base Score 35%Citation Network 65%

Base Score Contribution

0.524

From this paper's citation signal

Citation Network Contribution

0.982

From 18 citing papers with measurable signal

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Top 5 citers driving the network score

Ranked by each citer's contribution to N(p) — log1p(C_q) divided by its reference count — out of 26 citers.

Long COVID Classification: Findings from a Clustering Analysis in the Predi-COVID Cohort Study
International Journal of Environmental Research and Public Health202245 citations19 referencesContributes 0.202
A machine learning-based phenotype for long COVID in children: An EHR-based study from the RECOVER program
PLoS ONE202319 citations22 referencesContributes 0.136
Long COVID Classification: Findings from a Clustering Analysis in the Predi-COVID Cohort Study
Preprints.org20229 citations22 referencesContributes 0.105
Long COVID Incidence in a Large US Ambulatory Electronic Health Record System
American Journal of Epidemiology202330 citations34 referencesContributes 0.101
Impact of vaccination and variants of concern on long COVID clinical phenotypes
BMC Infectious Diseases202312 citations28 referencesContributes 0.092

Authors (30)

Hannah Blau ORCID,Timothy Bergquist ORCID,Johanna J. Loomba ORCID,Tiffany J. Callahan ORCID,Bryan Laraway ORCID

Live enrichment

PubMed:35665012 OpenAlex:https://openalex.org/W4281885828

NIGMS NIH HHS

Grant: U54 GM104938

NCATS NIH HHS

Grant: UL1 TR002649

NCATS NIH HHS

Grant: UL1 TR001433

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NIGMS NIH HHS

Grant: U54 GM104942

NCATS NIH HHS

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NIGMS NIH HHS

Grant: U54 GM115516

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NIGMS NIH HHS

Grant: U54 GM104940

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Bill & Melinda Gates Foundation

Grant: INV-018455

NCATS NIH HHS

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NIH HHS

Grant: R24 OD011883

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NHGRI NIH HHS

Grant: RM1 HG010860

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Grant: UL1 TR001430

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Grant: UL1 TR003142

greenLicense: cc-by-nc

repositoryhttps://www.medrxiv.org/content/medrxiv/early/2022/07/20/2022.05.24.22275398.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2022.05.24.22275398

repositoryhttps://doi.org/10.1101/2022.05.24.22275398

repositoryhttps://pubmed.ncbi.nlm.nih.gov/35665012

Fields of Study

Long-Term Effects of COVID-19Multiple Sclerosis Research StudiesCOVID-19 Clinical Research Studies

Keywords

Pairwise comparisonCluster analysisSimilarity (geometry)Artificial intelligenceComputer scienceGeneralizability theoryNatural historyCoronavirus disease 2019 (COVID-19)MedicineMachine learningData miningDiseaseStatisticsMathematicsInfectious disease (medical specialty)Internal medicine

Sustainable Development Goals

SDG 0: Good health and well-being

Additional file 1 of Impact of vaccination and variants of concern on long COVID clinical phenotypes

10.6084/m9.figshare.26641282.v1figshare

Additional file 1 of Impact of vaccination and variants of concern on long COVID clinical phenotypes

10.6084/m9.figshare.26641282figshare