Machine Learning-Integrated Explainable Artificial Intelligence Approach for Predicting Steroid Resistance in Pediatric Nephrotic Syndrome: A Metabolomic Biomarker Discovery Study.

Fatma Hilal Yagin, Feyza Inceoglu, Cemil Colak, Amal K Alkhalifa, Sarah A Alzakari, Mohammadreza Aghaei

Abstract read

In one paragraph

Article in Pharmaceuticals (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it

0cells of the map it votes in

2citing 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

2 citing papers in PubMed.

Artificial intelligence in pediatric nephrology: current applications and emerging frameworks for evidence generation.Pediatric nephrology (Berlin, Germany) · 2026
Review
Artificial Intelligence in Nephrology-State of the Art on Theoretical Background, Molecular Applications, and Clinical Interpretation.International journal of molecular sciences · 2026
Review

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.

Fatma Hilal YaginDepartment of Biostatistics, Faculty of Medicine, Malatya Turgut Ozal University, 44280 Malatya, Türkiye.ORCID 0000-0002-9848-7958

Feyza InceogluDepartment of Biostatistics, Faculty of Medicine, Malatya Turgut Ozal University, 44280 Malatya, Türkiye.ORCID 0000-0003-1453-0937

Cemil ColakDepartment of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, 44280 Malatya, Türkiye.ORCID 0000-0001-5406-098X

Amal K AlkhalifaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.ORCID 0000-0002-7273-4041

Sarah A AlzakariDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

Mohammadreza AghaeiDepartment of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology (NTNU), 6002 Alesund, Norway.ORCID 0000-0001-5735-3825

Funding

Princess Nourah bint Abdulrahman University PNURSP2025R721

6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

explainable artificial intelligencemetabolomic biomarker discoverypediatric nephrotic syndromesteroid resistance prediction

Identifiers

PMID41304904

DOI10.3390/ph18111659

PMCPMC12655726

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.