Evidence map›Paper›PMID 42846899›Full record

ReviewFrontiers in veterinary science2026

Chronic renal disease detection developments with future potential to improve veterinary clinical practice.

M Arevalo Turrubiarte, H Rideout, A D Whetton, N Geifman, S K Armstrong

Abstract readReview
In one paragraph

Review in Frontiers in veterinary science, 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

5 authors.

M Arevalo TurrubiarteFaculty of Health and Medical Sciences, School of Veterinary Medicine, Veterinary Health Engine (vHive), University of Surrey, Guildford, United Kingdom.
H RideoutFaculty of Health and Medical Sciences, School of Veterinary Medicine, Veterinary Health Engine (vHive), University of Surrey, Guildford, United Kingdom.
A D WhettonFaculty of Health and Medical Sciences, School of Veterinary Medicine, Veterinary Health Engine (vHive), University of Surrey, Guildford, United Kingdom.
N GeifmanFaculty of Health and Medical Sciences, School of Health Sciences, Veterinary Health Engine (vHive), University of Surrey, Guildford, United Kingdom.
S K ArmstrongFaculty of Health and Medical Sciences, School of Veterinary Medicine, Veterinary Health Engine (vHive), University of Surrey, Guildford, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronic Kidney Disease (CKD) is a major global human and veterinary health concern. Novel blood and urine biomarkers currently offer significant opportunities to advance early diagnosis, prognosis, and clinical outcomes. Adopting a translational medicine approach, we summarise research performed to date on CKD biomarkers in humans and relate these findings to companion animal CKD biomarker research. Emerging candidates, including microRNAs and multiprotein measurements for diagnostic signatures, are also considered. We discuss the potential complementarity offered by multi-omic approaches and Artificial Intelligence (AI) driven diagnostic algorithm generation for the understanding of CKD pathogenesis in dogs and cats.

Indexed as

biomarkerscaninediagnosticfelinehumankidneyveterinary

Identifiers

PMID42846899
PMCPMC13643920

What OpenQuestion holds

Textmetadata
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.