Evidence map›Paper›PMID 42015590›Full record

ArticleRenal failure2026

Decoding the renal-cochlear axis: explainable machine learning and phenotype clustering reveal high-risk hearing loss subtypes in CKD.

Ling Chen, Jing Wang, Guiqun Liu, Yu Zhao, Zhu Zhou, Qing Li

Abstract read
In one paragraph

Article in Renal failure, 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
–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

6 authors.

Ling ChenDepartment of Nephrology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.ORCID 0009-0003-4074-4565
Jing WangDepartment of Nephrology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
Guiqun LiuDepartment of Nephrology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
Yu ZhaoDepartment of Nephrology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
Zhu ZhouDepartment of Nephrology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
Qing LiDepartment of Nephrology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.ORCID 0000-0001-5452-2734

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study develops a dual-level machine learning framework for risk stratification and phenotyping of hearing loss (HL) in patients with chronic kidney disease (CKD) using data from the National Health and Nutrition Examination Survey (NHANES). From a cohort of 3,402 CKD patients, feature selection

Indexed as

Hearing LossMachine LearningRenal Insufficiency, ChronicAdultAgedBoosting Machine Learning AlgorithmsClassification AlgorithmsCluster AnalysisClustering AlgorithmsFemaleHumansLogistic ModelsMaleMiddle AgedNutrition SurveysPhenotypeChronic kidney diseasecluster analysishearing lossmachine learningpredictive modeling

Identifiers

PMID42015590
PMCPMC13103990

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.