Evidence map›Paper›PMID 38938760›Full record

ArticleNorth Carolina medical journal2024

Advancing Cardiovascular Risk Assessment with Artificial Intelligence: Opportunities and Implications in North Carolina.

Katherine M Conners, Christy L Avery, Faisal F Syed

Abstract read
In one paragraph

Article in North Carolina medical journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

3 authors.

Katherine M ConnersDepartment of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Christy L AveryDepartment of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Faisal F SyedDivision of Cardiology, University of North Carolina School of Medicine, Chapel Hill, North Carolina.

Funding

The Genetic Epidemiology of Heart, Lung, and Blood TraitsTraining Grant (GenHLB)T32HL129982 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Christy Leigh Avery, KAREN L. MOHLKE · 2016 to 2026
$3.8M
NHLBI NIH HHS T32 HL129982
6 · The paper itself

Abstract

Cardiovascular disease mortality is increasing in North Carolina with persistent inequality by race, income, and location. Artificial intelligence (AI) can repurpose the widely available electrocardiogram (ECG) for enhanced assessment of cardiac dysfunction. By identifying accelerated cardiac aging from the ECG, AI offers novel insights into risk assessment and prevention.

Indexed as

Artificial IntelligenceCardiovascular DiseasesElectrocardiographyHumansNorth CarolinaRisk Assessment

Identifiers

PMID38938760
PMCPMC11208038

What OpenQuestion holds

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LicenceCC BY-NC-SA
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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.