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Record W4297107649 · doi:10.3168/jds.2022-22299

Associations of calf management practices with antimicrobial use in Canadian dairy calves

2022· article· en· W4297107649 on OpenAlexafffundabout
Tamaki Uyama, D.L. Renaud, E.I. Morrison, J. Trenton McClure, S.J. LeBlanc, Charlotte B. Winder, Ellen de Jong, Kayley D. McCubbin, Herman W. Barkema, Simon Dufour, Javier Sánchez, Luke C. Heider, D.F. Kelton

Bibliographic record

VenueJournal of Dairy Science · 2022
Typearticle
Languageen
FieldVeterinary
TopicAnimal health and immunology
Canadian institutionsUniversity of Prince Edward IslandUniversité de MontréalUniversity of CalgaryUniversity of Guelph
FundersAgriculture and Agri-Food CanadaUniversité de MontréalPublic Health AgencyDairy Farmers of OntarioPublic Health Agency of CanadaDairy Farmers of Canada
KeywordsAntimicrobialColostrumAntimicrobial stewardshipHerdMedicineAnimal husbandryVeterinary medicineAnimal scienceBiologyAgricultureAntibioticsAntibiotic resistance

Abstract

fetched live from OpenAlex

The objective of this cross-sectional study was to investigate the relationship between management practices and antimicrobial use in heifer calves on Canadian dairy farms. Questionnaires on calf management practices, herd characteristics, and calf treatment records were administered on 147 dairy farms in 5 provinces during annual farm visits in a multiyear, nationwide research project (Canadian Dairy Network for Antimicrobial Stewardship and Resistance: CaDNetASR). Questions focused on the calf caregiver, calving pen, colostrum management, milk feeding, grouping, bedding management, and age when male calves were sold. Antimicrobial treatment records were collected on each farm from either an electronic herd management system or paper-based records. Newborn heifers born in the last 12 mo were identified retrospectively and followed to 60 d of age, with antimicrobial treatments and dates of sale or death extracted for further analysis. A multivariable linear regression model was developed with the natural log of the number of antimicrobial treatments per calf-year as the dependent variable, and categorized calf management practices and farm characteristics as the independent variables. A complete data set of records on 7,817 calves was retrieved from 74 farms based on completeness of calf records. A total of 2,310 calves were treated at least once with an antimicrobial, and 7,307 individual antimicrobial treatments were recorded. Among the reasons for antimicrobial use, respiratory disease (54%) was most common, followed by diarrhea (20%), presence of a fever (3%), and umbilical disease (2%). Florfenicol (33% of recorded treatments), penicillin (23%), and trimethoprim-sulfamethoxazole (18%) were commonly used, whereas fluoroquinolones (4%), and ceftiofur (1%) were used less commonly. Farms (31%) commonly had 0-1.0 antimicrobial treatments/calf-year (median: 2.2 treatments/calf-year; interquartile range: 0.64-6.43 treatments/calf-year). Defined daily dose (DDD) per calf-year was calculated based on the Canadian bovine standards. Among the 74 farms, florfenicol (1.35 DDD/calf-year) and macrolides (0.73 DDD/calf-year) were used most, whereas ceftiofur (0.008 DDD/calf-year) was the lowest. The final multivariable linear regression model indicated that farms that fed transition milk had fewer than half the number of antimicrobial treatments per calf-year than those who did not feed transition milk. The number of antimicrobial treatments per calf-year in preweaning calves was low on many farms, and there was low use of highly important drugs for human medicine. The effect of feeding transition milk should be investigated regarding potential effects on antimicrobial use and disease prevention.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.077
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.082
GPT teacher head0.360
Teacher spread0.278 · 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

Citations22
Published2022
Admission routes3
Has abstractyes

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