Evidence map›Paper›PMID 34397260›Full record

ArticleCirculation. Cardiovascular quality and outcomes2021

Use of Artificial Intelligence Tools Across Different Clinical Settings: A Cautionary Tale.

Konstantinos C Siontis, Peter A Noseworthy, Arman Arghami, Susan A Weston, Zachi I Attia, Juan A Crestanello, Paul A Friedman, Alanna M Chamberlain, Bernard J Gersh

Abstract readLetter
In one paragraph

Article in Circulation. Cardiovascular quality and outcomes, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Konstantinos C SiontisDepartment of Cardiovascular Medicine (K.C.S., P.A.N., Z.I.A., P.A.F., B.J.G.), Mayo Clinic, Rochester, MN.ORCID 0000-0002-6658-0971
Peter A NoseworthyDepartment of Cardiovascular Medicine (K.C.S., P.A.N., Z.I.A., P.A.F., B.J.G.), Mayo Clinic, Rochester, MN.
Arman ArghamiDepartment of Cardiovascular Surgery (A.A., J.A.C.), Mayo Clinic, Rochester, MN.ORCID 0000-0002-7398-0738
Susan A WestonDepartment of Quantitative Health Sciences (S.A.W., A.M.C.), Mayo Clinic, Rochester, MN.
Zachi I AttiaDepartment of Cardiovascular Medicine (K.C.S., P.A.N., Z.I.A., P.A.F., B.J.G.), Mayo Clinic, Rochester, MN.ORCID 0000-0002-9706-7900
Juan A CrestanelloDepartment of Cardiovascular Surgery (A.A., J.A.C.), Mayo Clinic, Rochester, MN.ORCID 0000-0001-5415-1478
Paul A FriedmanDepartment of Cardiovascular Medicine (K.C.S., P.A.N., Z.I.A., P.A.F., B.J.G.), Mayo Clinic, Rochester, MN.
Alanna M ChamberlainDepartment of Quantitative Health Sciences (S.A.W., A.M.C.), Mayo Clinic, Rochester, MN.ORCID 0000-0003-1888-9584
Bernard J GershDepartment of Cardiovascular Medicine (K.C.S., P.A.N., Z.I.A., P.A.F., B.J.G.), Mayo Clinic, Rochester, MN.ORCID 0000-0001-5605-900X

Funding

Rochester Epidemiology ProjectR01AG034676 · NIA · MAYO CLINIC ROCHESTER · PI ROCCA, WALTER A, ST SAUVER, JENNIFER LYNN · 2010 to 2019
$9.5M
Interdisciplinary Infrastructure for Aging Research: Rochester Epidemiology ProjectR33AG058738 · NIA · MAYO CLINIC ROCHESTER · PI LEBRASSEUR, NATHAN K, OLSON, JANET E · 2020 to 2022
$2.4M
Multimorbidity, Rurality, and Outcomes in Atrial Fibrillation in the CommunityR21AG062580 · NIA · MAYO CLINIC ROCHESTER · PI CHAMBERLAIN, ALANNA M · 2019 to 2020
$451k
NIA NIH HHS R01 AG034676NIA NIH HHS R21 AG062580NIA NIH HHS R33 AG058738
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

Atrial FibrillationDeep LearningArtificial IntelligenceHumansatrial fibrillationdeep learningprobability

Identifiers

PMID34397260
PMCPMC8455449

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.