Evidence map›Paper›PMID 40879754›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2026

The importance of developing multiparametric prognostic scores to stratify coronary risk by means of artificial intelligence.

Guillermo Romero-Farina, Santiago Aguadé-Bruix, C David Cooke, Ernest V Garcia

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Article in European journal of nuclear medicine and molecular imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Guillermo Romero-FarinaNuclear Medicine DepartmentVall d'Hebron University Hospital, Universitat Autònoma de Barcelona, Barcelona, Spain. guiromfar@gmail.com.ORCID 0000-0003-4404-6337
Santiago Aguadé-BruixNuclear Medicine DepartmentVall d'Hebron University Hospital, Universitat Autònoma de Barcelona, Barcelona, Spain.
C David CookeDepartment of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA.
Ernest V GarciaDepartment of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cardiovascular risk stratification is crucial, as it is a key predictor of morbidity and mortality. The development of multiparametric scores for coronary risk stratification, integrated with artificial intelligence (AI), is important because it facilitates assessment in clinical practice. Therefore, prognostic coronary risk scores that incorporate multiple clinical variables and cardiac imaging data are necessary and deserve greater attention, as they provide a more comprehensive and accurate evaluation of individual patient risk across various clinical scenarios. Additionally, they support clinicians in making better-informed decisions based on a comprehensive assessment. Importantly, the widespread clinical use of multiparametric risk scores should be enabled by implementing standardized computer interfaces that can exchange the relevant imaging and clinical data needed to calculate these scores. The ongoing AI revolution, which increasingly relies on digital demographic, clinical, and imaging data, is rapidly making the availability of such data a reality.

Indexed as

Artificial IntelligenceCoronary Artery DiseaseHumansPrognosisRisk AssessmentAICoronary riskGated PETGated SPECTVHRS

Identifiers

What Socratic holds

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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 Socratic graph.