Evidence map›Paper›PMID 42147060›Full record

ArticleFrontiers in cardiovascular medicine2026

Evaluation of coronary heart disease risk prediction based on simple physical examination parameters by machine learning model: a retrospective cohort model development and validation study.

Hui Xiong, Xiang Cao, Xiao Han, Jia-Xing Zhang, Jia-Rui Zhuang, Shuai He, Min Zhu, Ji Li, Wei Qin

Abstract read
In one paragraph

Article in Frontiers in cardiovascular medicine, 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

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

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

9 authors.

Hui XiongDepartment of Cardiothoracic Surgery, Affiliated Hospital of Nantong University, Nantong, Jiangsu, China.
Xiang CaoDepartment of Cardiothoracic Surgery, Affiliated Hospital of Nantong University, Nantong, Jiangsu, China.
Xiao HanDepartment of Cardiothoracic Surgery, Affiliated Hospital of Nantong University, Nantong, Jiangsu, China.
Jia-Xing ZhangDepartment of Cardiothoracic Surgery, Affiliated Hospital of Nantong University, Nantong, Jiangsu, China.
Jia-Rui ZhuangDepartment of Anesthesiology, Affiliated Hospital of Nantong University, Nantong, Jiangsu, China.
Shuai HeDepartment of Cardiothoracic Surgery, Affiliated Hospital of Nantong University, Nantong, Jiangsu, China.
Min ZhuNantong Vocational University, Nantong, Jiangsu, China.
Ji LiNortheastern University, Shenyang, China.
Wei QinDepartment of Cardiothoracic Surgery, Affiliated Hospital of Nantong University, Nantong, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: To develop and externally validate a coronary heart disease (CHD) risk model from routine clinical indicators and identify key predictors. Methods: The Framingham Heart Study cohort ( Results: Internal validation yielded AUC 0.977 and accuracy 0.942 ( Conclusion: A model built from routinely available measures demonstrates strong discrimination for CHD risk and generalizes to an external cohort, offering a clinically interpretable tool for cardiovascular risk assessment.

Indexed as

clinical indicatorscoronary heart diseasemachine learningrisk predictionshap

Identifiers

PMID42147060
PMCPMC13171337

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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.