Evidence map›Paper›PMID 41245264›Full record

ArticleFrontiers in physiology2025

Salp swarm-optimized machine learning models for predicting preoperative aortic rupture risk in acute type a aortic dissection patients.

Haiyue Bao, Lijun Sun, GuanQing Cui, Shihao Cai, Weiliang Zheng, Hua Peng, Chenhui Yang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in physiology, 2025. 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
–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

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

7 authors.

Haiyue Bao *National Institute for Data Science in Health and Medicine, School of Medicine, Xiamen University, Xiamen, China.
Lijun Sun *Department of Intensive Care Unit, Xiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
GuanQing CuiSchool of Informatics, Xiamen University, Xiamen, China.
Shihao CaiDepartment of Cardiac Surgery, Xiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
Weiliang ZhengDepartment of Cardiac Rehabilitation, Xiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
Hua PengDepartment of Cardiac Surgery, Xiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
Chenhui YangNational Institute for Data Science in Health and Medicine, School of Medicine, Xiamen University, Xiamen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Acute Type A aortic dissection (ATAAD) is characterized by acute onset and rapid progression, with aortic rupture due to dissection extension being the primary lethal mechanism. Timely identification of high-risk patients is critical for prioritizing surgical intervention to reduce rupture incidence. This study aimed to develop and validate an interpretable machine learning model to predict aortic rupture in ATAAD patients, thereby improving risk classification and supporting clinical decisions. Medical records of ATAAD patients from Xiamen Cardiovascular Hospital (January 2019-October 2024) were retrospectively analyzed. Predictors were screened via statistical significance (p

Indexed as

clinical decision supportmachine learningrupture risk predictionsalp swarm optimization algorithmtype A aortic dissection

Identifiers

PMID41245264
PMCPMC12614460

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.