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Principles of Large-scale Evidence Generation and Evaluation across a Network of Databases (LEGEND)

Journal of the American Medical Informatics Association(2020)10.1093/jamia/ocaa103Source: DataRank Database

Principles of Large-scale Evidence Generation and Evaluation across a Network of Databases (LEGEND) is a research paper published in Journal of the American Medical Informatics Association (2020). On theSindex it has a DataRank of 0. It has been cited 51 times.

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Scored on demand from live citation data

Open Access51 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (6 grants)OA: hybridTopics (44)IDs (PubMed, PMC)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (9)

Patrick Ryan ORCID,Nicole Pratt ORCID,Ruijun Chen ORCID,Seng Chan You ORCID,Harlan M. Krumholz ORCID

Live enrichment

PubMed:32909033 PMC:PMC7481029

National Science Foundation

Grant: IIS 1251151

National Institutes of Health

Grant: R01 LM006910

Australian National Health and Medical Research Council

Grant: GNT1157506

NCATS NIH HHS

Grant: UL1 TR001863

National Institutes of Health

Grant: 2R01LM006910-14

Discovering and Applying Knowledge in Clinical Databases

National Science Foundation

Grant: 1251151

BIGDATA: Small: DA: Patient-level predictive modeling from massive longitudinal databases

hybridLicense: cc-by-nc

publisherhttps://academic.oup.com/jamia/article-pdf/27/8/1331/34152911/ocaa103.pdf

publisherhttp://academic.oup.com/jamia/article-pdf/27/8/1331/34152911/ocaa103.pdf

repositoryhttps://escholarship.org/uc/item/954642xw

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/7481029

repositoryhttps://hdl.handle.net/11541.2/145402

Fields of Study

03 medical and health sciences0302 clinical medicine

MeSH Terms

HumansHypertensionAntihypertensive AgentsTreatment OutcomeConfidence IntervalsData Interpretation, StatisticalObservationComputer Communication NetworksDatabases, FactualMeta-Analysis as TopicRandomized Controlled Trials as TopicPropensity Score

Keywords

Observational StudiesTreatment EffectsOpen ScienceEmpirical CalibrationData InterpretationDatabases, Factual610ObservationMedical and Health SciencesDatabasesComputer Communication NetworksEngineeringMeta-Analysis as TopicInformation and Computing SciencesConfidence IntervalsHumansPropensity ScoreFactualAntihypertensive AgentsRandomized Controlled Trials as TopicStatistical8.4 Research design and methodologies (health services)Good Health and Well BeingTreatment OutcomeData Interpretation, StatisticalPerspectiveHypertensionGeneric health relevanceMedical InformaticsHealth and social care services research

Sustainable Development Goals

SDG 3: 3. Good health