Evidence map›Paper›PMID 42012815›Full record

ArticleJournal of veterinary internal medicine2026

Inter-rater agreement of a newborn calf lung ultrasound scoring system.

Ana C A Abreu, Sébastien Buczinski, Gabriela S Fregolon, Jobson F P Cajueiro, Viviani Gomes

Abstract read
In one paragraph

Article in Journal of veterinary internal medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Ana C A AbreuDepartment of Internal Medicine, College of Veterinary Medicine and Animal Science, University of São Paulo, São Paulo 05508-270, Brazil.ORCID 0000-0003-3722-7412
Sébastien BuczinskiDépartement Des Sciences Cliniques, Faculté de Médecine Vétérinaire, Université de Montréal, Saint- Hyacinthe J2S 2M2, Québec, Canada.ORCID 0000-0002-8460-4885
Gabriela S FregolonDepartment of Internal Medicine, College of Veterinary Medicine and Animal Science, University of São Paulo, São Paulo 05508-270, Brazil.ORCID 0009-0007-9236-5689
Jobson F P CajueiroDepartment of Internal Medicine, College of Veterinary Medicine and Animal Science, University of São Paulo, São Paulo 05508-270, Brazil.ORCID 0000-0003-3228-2383
Viviani GomesDepartment of Internal Medicine, College of Veterinary Medicine and Animal Science, University of São Paulo, São Paulo 05508-270, Brazil.ORCID 0000-0002-4553-2276

Funding

São Paulo Research Foundation (FAPESP) #2024/18148-9
6 · The paper itself

Abstract

backgroundLung ultrasonography (LUS) is a reliable and noninvasive tool for detecting pulmonary abnormalities in neonatal calves. Although scoring systems for clinical decision-making are well established in human neonatology, equivalent validated systems for newborn calves are lacking for the perinatal period (0-48 h). HYPOTHESIS/

objectivesAssess inter-observer agreement among veterinarians regarding a proposed lung ultrasound scoring system for newborn calves adapted from human neonatology score to detect pulmonary problems soon after birth. ANIMALS: Fifty thoracic ultrasound items (25 images and 25 videos) from calves born in commercial Brazilian dairy herds were evaluated by 14 veterinarians with skills in calf LUS from Brazil, Canada, France, and the United States.

methodsAfter a 5-min instructional video, raters scored each item (0-4) based on lung aeration and consolidation patterns and graded media quality and diagnostic confidence on a 1-5 Likert scale. Agreement was assessed using raw percentage agreement (PA), Gwet's AC2, Krippendorff's alpha (α), and weighted Fleiss' kappa (κ).

resultsInter-rater reliability for the 0-4 LUS score was very good: PA = 0.957 (95% confidence interval [CI], 0.940-0.973), AC2 = 0.845 (95% CI, 0.781-0.909), α = 0.839 (95% CI, 0.827-0.852), and κ = 0.849 (95% CI, 0.736-0.903). Median quality and diagnostic confidence were 4 (interquartile range [IQR] = 4-5). When restricted to high-confidence loops (scores 4-5), agreement increased slightly (PA = 0.960; AC2 = 0.880; α = 0.860). CONCLUSIONS AND CLINICAL IMPORTANCE: The high inter-observer agreement confirmed the reliability of the newborn calf LUS score. This score therefore potentially could be further validated to follow lung changes after delivery in neonatal calves.

Indexed as

Cattle DiseasesLungLung DiseasesAnimalsAnimals, NewbornCattleFemaleObserver VariationReproducibility of ResultsUltrasonographydiagnostic imaginglung clearanceneonate calvesobserver agreementperinate calfpoint-of-care

Identifiers

PMID42012815
PMCPMC13098365

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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.