Evidence map›Paper›PMID 41069034›Full record

ArticleRenal failure2025

Machine learning model predicts clotting risk during CRRT in ESKD patients: a SHAP-interpretable approach.

Shuang Qiu, Shibo Mu, Yongyuan Tao, Ning Zhang, Jiuxu Bai, Ning Cao

Abstract read
In one paragraph

Article in Renal failure, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Shuang QiuDepartment of Blood Purification, General Hospital of Northern Theatre Command, Shenyang, China.
Shibo MuSchool of Mechanical Engineering, Shenyang University of Technology, Shenyang, China.
Yongyuan TaoDepartment of Blood Purification, General Hospital of Northern Theatre Command, Shenyang, China.
Ning ZhangDepartment of Blood Purification, General Hospital of Northern Theatre Command, Shenyang, China.
Jiuxu BaiDepartment of Blood Purification, General Hospital of Northern Theatre Command, Shenyang, China.
Ning CaoDepartment of Blood Purification, General Hospital of Northern Theatre Command, Shenyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ensuring fluent extracorporeal circulation and preventing circuit clotting are important for end-stage kidney disease (ESKD) patients undergoing continuous renal replacement therapy (CRRT). This study aimed to develop a predictive model using machine learning (ML) algorithms to evaluate clotting risk after initiating CRRT, enhancing treatment safety and effectiveness. This study involved 636 ESKD patients who underwent CRRT. Feature selection was conducted

Indexed as

Continuous Renal Replacement TherapyKidney Failure, ChronicMachine LearningThrombosisAdultAgedAlgorithmsArea Under CurveBlood CoagulationDecision TreesFemaleHumansLogistic ModelsMaleMiddle AgedRetrospective Studiescontinuous renal replacement therapyESKDextracorporeal circuit clottingmachine learningprediction model

Identifiers

PMID41069034
PMCPMC12517423

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.