Evidence map›Paper›PMID 40901064›Full record

ArticleFrontiers in veterinary science2025

Inter-evaluator bias and applicability of feline body condition score from visual assessment.

Emily C Graff, Christopher R Lea, Diane Delmain, Erin D Chamorro, Xiaolei Ma, Jingyi Zheng, Yue Zhang, Emily Brinker, Kenzii Kittell, Mackenzie Hicks and 5 more

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

15 authors.

Emily C GraffDepartment of Pathobiology, College of Veterinary Medicine, Auburn University, Auburn, AL, United States.
Christopher R LeaDepartment of Clinical Sciences, College of Veterinary Medicine, Auburn University, Auburn, AL, United States.
Diane DelmainDepartment of Clinical Sciences, College of Veterinary Medicine, Auburn University, Auburn, AL, United States.
Erin D ChamorroDepartment of Clinical Sciences, College of Veterinary Medicine, Auburn University, Auburn, AL, United States.
Xiaolei MaDepartment of Pathobiology, College of Veterinary Medicine, Auburn University, Auburn, AL, United States.
Jingyi ZhengDepartment of Mathematics and Statistics, College of Science and Mathematics, Auburn University, Auburn, AL, United States.
Yue ZhangDepartment of Pathobiology, College of Veterinary Medicine, Auburn University, Auburn, AL, United States.
Emily BrinkerDepartment of Comparative Pathobiology, Cummings School of Veterinary Medicine, Tufts University, North Grafton, MA, United States.
Kenzii KittellVeterinary Education Center, College of Veterinary Medicine, Auburn University, Auburn, AL, United States.
Mackenzie HicksVeterinary Education Center, College of Veterinary Medicine, Auburn University, Auburn, AL, United States.
Casey PfisterVeterinary Education Center, College of Veterinary Medicine, Auburn University, Auburn, AL, United States.
Heather HamiltonVeterinary Education Center, College of Veterinary Medicine, Auburn University, Auburn, AL, United States.
Qinghong LiDepartment of Pathobiology, College of Veterinary Medicine, Auburn University, Auburn, AL, United States.
Douglas R MartinScott-Ritchey Research Center, College of Veterinary Medicine, Auburn University, Auburn, AL, United States.
Xu WangDepartment of Pathobiology, College of Veterinary Medicine, Auburn University, Auburn, AL, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Body Condition Score (BCS) is an effective tool for assessing body weight and fat mass, as well as diagnosing obesity and abnormal weight loss. A method for visual assessment of BCS in cats would be useful to expand access for feline health and research. The goal of this study is to determine whether BCS can be accurately assessed solely from photographs of cats, and to measure inter-evaluator bias in visually assessed BCS. To do this, a set of online-sourced cat images was administered as a quiz to nine evaluators. Inter-evaluator bias was relatively low (mean ± SE = 0.35 ± 0.03) with ~50% complete agreement. To validate the results, a BCS was clinically assessed during routine wellness exams for 38 cats, enrolled, through palpation by one evaluator and visual assessment by all nine evaluators using photographs collected at the exam. The visual assessment of BCS deviated from the clinically assessed BCS by 0.61 ± 0.04, which was slightly higher than the deviation observed in the online-sourced image set. In both scenarios, the majority voting among all evaluators achieved the highest accuracy, demonstrating its effectiveness in reducing evaluator bias. Inter-evaluator bias caused a 15.5% misclassification between ideal and overweight BCS but 1.8% between ideal and obese, indicating minimal bias in diagnosing feline obesity. The ability to accurately assess BCS through photographic evaluation will enhance remote consultations in telemedicine and support large-scale epidemiological studies. This study has developed a method for evaluating and minimizing inter-evaluator bias in BCS assessments across diverse practitioners and settings, thereby improving consistency and comparability and improving our understanding and application of BCS as a tool for feline health.

Indexed as

body condition scorebody weightdomestic catobesitypalpationtelemedicine

Identifiers

PMID40901064
PMCPMC12400680

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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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.