Evidence map›Paper›PMID 42225904›Full record

ArticleScientific reports2026

A unified framework of information measures for complex linear Diophantine fuzzy sets with application to chronic kidney disease.

Abdul Wahab Mustafa, Zia Bashir, Jawad Ali, Muhammed I Syam

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 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

4 authors.

Abdul Wahab MustafaDepartment of Mathematics, Quaid-i-Azam University, Islamabad, Pakistan.
Zia BashirDepartment of Mathematics, Quaid-i-Azam University, Islamabad, Pakistan.
Jawad AliSchool of Computer and Artificial Intelligence, Shandong Jianzhu University, Jinan, 250101, China.
Muhammed I SyamDepartment of Mathematical Sciences, United Arab Emirates University, P. O. Box 15551, Al-Ain, UAE. m.syam@uaeu.ac.ae.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In many real-world decision-making problems, uncertainty extends beyond conventional fuzzy representations and often involves structural, temporal, and phase-dependent characteristics. Existing fuzzy set models, including their extensions, are generally limited in their ability to simultaneously capture both the magnitude and dynamic behavior of uncertain information. This paper develops a comprehensive framework of information measures for complex linear Diophantine fuzzy sets (C

Indexed as

Fuzzy LogicRenal Insufficiency, ChronicAlgorithmsHumansSoft ComputingUncertaintyChronic kidney diseaseComplex linear Diophantine fuzzy setEntropy measureInclusion measureSimilarity measure

Identifiers

PMID42225904
PMCPMC13458061

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.