Evidence map›Paper›PMID 40400671›Full record

ArticleFrontiers in veterinary science2025

Gaining insights into pet owner understanding/lived experience of canine chronic kidney disease using survey and social media data.

Georgina Tarrant, Luke Boyden, Taranpreet Rai, Andrea Wright, Alasdair J C Cook, Kevin Wells

Abstract read
In one paragraph

Article in Frontiers in veterinary science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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.

Georgina Tarrant *Surrey DataHub, vHive, School of Veterinary Medicine, University of Surrey, Guildford, United Kingdom.
Luke Boyden *Surrey DataHub, vHive, School of Veterinary Medicine, University of Surrey, Guildford, United Kingdom.
Taranpreet RaiSurrey DataHub, vHive, School of Veterinary Medicine, University of Surrey, Guildford, United Kingdom.
Andrea WrightOutcomes Research, Zoetis, Loughlinstown, Dublin, Ireland.
Alasdair J C CookSurrey DataHub, vHive, School of Veterinary Medicine, University of Surrey, Guildford, United Kingdom.
Kevin WellsSurrey DataHub, vHive, School of Veterinary Medicine, University of Surrey, Guildford, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Chronic kidney disease (CKD) is a common and progressive condition in dogs characterized by irreversible damage to one or both kidneys over an extended period leading to gradual decline in kidney function. Early diagnosis is crucial to improve quality of life and increase survival through medical interventions. Methods: This study investigated pet owner understanding of this condition using insights gained by comparing pet owner survey responses with bulk harvested social media discussions on canine CKD. We combined structured survey data ( Results: Both data sources highlighted increased urination and water consumption as primary pet owner concerns, with these clinical signs showing moderate positive correlation (Pearson correlation coefficient of Discussion: The complementary nature of structured surveys and social media analysis provided richer understanding of pet owner experiences, understanding and management of CKD. This combined methodological approach offers a model for investigating other chronic conditions in veterinary medicine.

Indexed as

AIcanine chronic kidney diseasecanine chronic renal failurecanine CKDcanine CRFlarge language modelssocial media analysissurvey data

Identifiers

PMID40400671
PMCPMC12094028

What OpenQuestion holds

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LicenceCC BY
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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.