Evidence map›Paper›PMID 41261018›Full record

SynthesisRenal failure2025

Systematic review and comparison of machine learning and conventional statistical models for predicting cardiovascular events in dialysis patients.

Yifei Lu, Canyu Chen, Junxiang Qiu, Qiuchen Ji, Liang Zhou, Honglin Xiong, Tao Wu

Abstract readSystematic ReviewComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Renal failure, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. 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

7 authors.

Yifei LuSchool of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Canyu ChenSchool of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Junxiang QiuCollaborative Innovation Center for Biomedicine, Shanghai University of Medicine & Health Sciences, Shanghai, China.
Qiuchen JiInstitute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Liang ZhouCollaborative Innovation Center for Biomedicine, Shanghai University of Medicine & Health Sciences, Shanghai, China.
Honglin XiongCollaborative Innovation Center for Biomedicine, Shanghai University of Medicine & Health Sciences, Shanghai, China.
Tao WuSchool of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This systematic review aimed to evaluate the performance of machine learning (ML) models and conventional statistical models (CSMs) for predicting cardiovascular events in dialysis patients. Following PRISMA guidelines, eligible studies were searched through PubMed and Embase (January 2015-March 2025). Model performance (AUC/C-index) was compared using Mann-Whitney U test, while risk of bias was assessed

Indexed as

Cardiovascular DiseasesKidney Failure, ChronicMachine LearningModels, StatisticalRenal DialysisDeep LearningHumansRisk AssessmentCardiovascular ComplicationsDialysisLogistic RegressionMachine LearningPredictive ModelsPROSPERO ID: CRD420251020630

Identifiers

PMID41261018
PMCPMC12632218

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.