ArticleEuropean journal of nuclear medicine and molecular imaging2026
The importance of developing multiparametric prognostic scores to stratify coronary risk by means of artificial intelligence.
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.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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
Identifiers
40879754What Socratic holds
Registered trials
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.