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Predicting recurrent interventions after radiocephalic arteriovenous fistula creation with machine learning and the PREDICT-AVF web app

The Journal of Vascular Access(2023)10.1177/11297298231203356Source: DataRank Database

Predicting recurrent interventions after radiocephalic arteriovenous fistula creation with machine learning and the PREDICT-AVF web app is a research paper published in The Journal of Vascular Access (2023). On theSindex it has a DataRank of 0. It has been cited 11 times.

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11 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (2 grants)Impact (FWCI 3.21)OA: closedTopics (45)Linked Datasets (6)DataCite Reuse (6)IDs (PubMed, OpenAlex)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (2/2)

Has DOI
Indexed in repositories

Accessible (0/2)

Interoperable (2/2)

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Reusable (0/3)

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Authors (8)

Tanujit Dey ORCID,James J. Fitzgibbon,Muhammad Mamdani ORCID,Dirk M. Hentschel ORCID,Michael Belkin

Live enrichment

PubMed:38143431 OpenAlex:https://openalex.org/W4390196778

national institutes of health

Grant: T32 HL007734

Brigham and Women’s Hospital Heart and Vascular Center Faculty Award

FWCI

3.21

Citation Percentile

0.9%

Citation Trend

2024

2025

2026

closedLicense: https://journals.sagepub.com/page/policies/text-and-data-mining-license

publisherhttps://journals.sagepub.com/doi/pdf/10.1177/11297298231203356

publisherhttps://journals.sagepub.com/doi/full-xml/10.1177/11297298231203356

journalhttps://doi.org/10.1177/11297298231203356

repositoryhttps://pubmed.ncbi.nlm.nih.gov/38143431

Fields of Study

Central Venous Catheters and HemodialysisArtificial Intelligence in Healthcare and EducationSurgical Simulation and Training

MeSH Terms

Clinical Decision-MakingMachine LearningAdultAgedArteriovenous Shunt, SurgicalCanadaDecision Support TechniquesFemaleGraft Occlusion, VascularRenal DialysisHumansMaleMiddle AgedPredictive Value of TestsProspective StudiesRecurrenceReoperationRisk FactorsTime FactorsUnited StatesVascular PatencyVeinsTreatment OutcomeRadial ArteryRisk AssessmentUpper ExtremityMobile Applications

Keywords

MedicinePsychological interventionArteriovenous fistulaCohortLogistic regressionProportional hazards modelPhysical therapySurgeryInternal medicineChronic Kidney DiseaseMachine LearningHemodialysis AccessWeb ApplicationEnd-stage Kidney DiseaseClinical Risk Prediction

Sustainable Development Goals

SDG 0: Gender equality

sj-jpg-3-jva-10.1177_11297298231203356 – Supplemental material for Predicting recurrent interventions after radiocephalic arteriovenous fistula creation with machine learning and the PREDICT-AVF web app

10.25384/sage.24902862SAGE Journals

sj-eps-2-jva-10.1177_11297298231203356 – Supplemental material for Predicting recurrent interventions after radiocephalic arteriovenous fistula creation with machine learning and the PREDICT-AVF web app

10.25384/sage.24902859SAGE Journals

sj-docx-1-jva-10.1177_11297298231203356 – Supplemental material for Predicting recurrent interventions after radiocephalic arteriovenous fistula creation with machine learning and the PREDICT-AVF web app

10.25384/sage.24902856SAGE Journals

10.25384/sage.24902862.v1SAGE Journals

10.25384/sage.24902856.v1SAGE Journals

10.25384/sage.24902859.v1SAGE Journals

Predicting recurrent interventions after radiocephalic arteriovenous fistula creation with machine learning and the PREDICT-AVF web app

FAIR Checklist

Authors (8)

Funding (2 grants)

Impact

Open Access

Topics

Linked Datasets (6)