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Record W3196867335 · doi:10.3168/jds.2021-20503

Evaluation of inter-rater agreement of the clinical signs used to diagnose bovine respiratory disease in individually housed veal calves

2021· article· en· W3196867335 on OpenAlexafffundabout
Julie Berman, David Francoz, Abdelmonem Abdallah, Simon Dufour, Sébastien Buczinski

Bibliographic record

VenueJournal of Dairy Science · 2021
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMicrobial infections and disease research
Canadian institutionsUniversité de MontréalCegep de Saint Hyacinthe
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesMinistère de l'Agriculture, des Pêcheries et de l'Alimentation
KeywordsBovine respiratory diseaseMedicineHead tiltNasal dischargeVital signsRectal temperatureClinical diseaseRespiratory systemKappaDiseaseAnesthesiaInternal medicineSurgery

Abstract

fetched live from OpenAlex

In dairy calves raised for veal, typical clinical signs of bovine respiratory disease (BRD) are ocular discharge, nasal discharge, ear droop or head tilt, abnormal respiration, cough, and increased rectal temperature. Despite the existence of several clinical scoring systems, there are few studies on the variability of human recognition of individual BRD clinical signs. The objective of this study was therefore to assess the inter-rater agreement of BRD clinical signs in veal calves. We hypothesized that BRD clinical signs were not detected equally between veterinarians, technicians, and producers of the veal industry and that some clinical signs have higher inter-rater agreement than others. During 2017-2018, we prospectively recorded 524 videos of physical examinations of random veal calves from 48 different batches in Québec, Canada. A researcher, not involved in the inter-rater assessment, classified each video as presence/absence of each BRD clinical sign except rectal temperature. For each of the 5 clinical signs, 15 videos with and 15 videos without the clinical signs were randomly selected to avoid kappa paradoxes. Those 30 videos were then presented in a random order to experienced raters of BRD in veal calves: 6 veterinarians, 6 technicians, and 6 producers. The raters assessed the clinical signs using scores based on the Wisconsin and California scoring system with modifications (0 = absent, 1 = mild, 2 = moderate, 3 = severe for nasal discharge, ocular discharge, and ear droop or head tilt; and 0 = absent, 1 = moderate, 2 = severe for abnormal respiration and induced cough). We used median percentage agreement (Pa), median Cohen's kappa (κ), and Gwet's agreement coefficient 1 (AC1) to assess inter-rater agreement. The effect of scale combination was also tested to determine the optimal combination (4-scale 0/1/2/3 vs. 3-scale 0/1/2 vs. 2-scale 0/1,2,3; 0,1/2,3; or 0/1,2). The differences of inter-rater agreement between veterinarians, technicians, and producers were estimated by a Wilcoxon rank-sum test. The 2-scale combination (0,1/2,3 or 0/1,2) had the highest inter-rater agreement for all clinical signs. With this combination, induced cough was the clinical sign with the highest inter-rater agreement (Pa = 0.93; κ = 0.79; AC1 = 0.87) and abnormal respiration was the sign with the lowest inter-rater agreement (Pa = 0.77; κ = 0.20; AC1 = 0.74). According to Pa and AC1 values, the 2-scale inter-rater agreement of the 5 clinical signs was good (value > 0.6). According to κ, only ear droop or head tilt and induced cough had a substantial 2-scale inter-rater agreement (κ > 0.6). In general, the 2-scale inter-rater agreement was better among veterinarians than among technicians and producers, except for the ear droop/head tilt, where agreement was better among producers. We concluded that with severity scores assessed on a scale of 2 (0,1/2,3 or 0/1,2), the inter-rater agreement of BRD clinical signs was variable according to the sign in veal calves. BRD clinical signs were not detected equally between veterinarians, technicians, and producers of the veal industry. Future research could determine if this discrepancy could be improved by standardization training.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.051
metaresearch head score (Gemma)0.084
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.271

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0510.084
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.123
GPT teacher head0.411
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations30
Published2021
Admission routes3
Has abstractyes

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Same venueJournal of Dairy ScienceSame topicMicrobial infections and disease researchFrench-language works237,207