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Record W2118842113 · doi:10.3168/jds.2012-5713

Herd-level relationship between antimicrobial use and presence or absence of antimicrobial resistance in gram-negative bovine mastitis pathogens on Canadian dairy farms

2013· article· en· W2118842113 on OpenAlexafffundabout
Vineet Saini, J. Trenton McClure, Daniel T. Scholl, T.J. DeVries, Herman W. Barkema

Bibliographic record

VenueJournal of Dairy Science · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsUniversity of GuelphUniversity of CalgaryUniversity of Prince Edward IslandAlberta Health Services
FundersAgriculture and Agri-Food CanadaNovalaitUniversité de MontréalPublic Health AgencyPublic Health Agency of CanadaDairy Farmers of Canada
KeywordsAntimicrobialPenicillinMastitisAmpicillinCefoxitinMicrobiologyAntibiotic resistanceVeterinary medicineTetracyclineTrimethoprimBiologyCeftiofurCloxacillinHerdMedicineAntibioticsBacteriaStaphylococcus aureus

Abstract

fetched live from OpenAlex

Concurrent data on antimicrobial use (AMU) and resistance are needed to contain antimicrobial resistance (AMR) in bacteria. The present study examined a herd-level association between AMU and AMR in Escherichia coli (n=394) and Klebsiella species (n=139) isolated from bovine intramammary infections and mastitis cases on 89 dairy farms in 4 regions of Canada [Alberta, Ontario, Québec, and Maritime Provinces (Prince Edward Island, Nova Scotia, and New Brunswick)]. Antimicrobial use data were collected using inventory of empty antimicrobial containers and antimicrobial drug use rate was calculated to quantify herd-level AMU. Minimum inhibitory concentrations (MIC) were determined using Sensititre National Antimicrobial Resistance Monitoring System (NARMS) gram-negative MIC plate (Trek Diagnostic Systems Inc., Cleveland, OH). Isolates were classified as susceptible, intermediate, or resistant. Intermediate and resistant category isolates were combined to form an AMR category, and multivariable logistic regression models were built to determine herd-level odds of AMR to tetracycline, ampicillin, cefoxitin, chloramphenicol, trimethoprim-sulfamethoxazole combination, sulfisoxazole, streptomycin and kanamycin in E. coli isolates. In the case of Klebsiella species isolates, logistic regression models were built for tetracycline and sulfisoxazole; however, no associations between AMU and AMR in Klebsiella species were observed. Ampicillin-intermediate or -resistant E. coli isolates were associated with herds that used intramammarily administered cloxacillin, penicillin-novobiocin combination, and cephapirin used for dry cow therapy [odds ratios (OR)=26, 32, and 189, respectively], and intramammary ceftiofur administered for lactating cow therapy and systemically administered penicillin (OR=162 and 2.7, respectively). Use of systemically administered penicillin on a dairy farm was associated with tetracycline and streptomycin-intermediate or -resistant E. coli isolates (OR=5.6 and 2.8, respectively). Use of cephapirin and cloxacillin administered intramammarily for dry cow therapy was associated with increasing odds of having at least 1 kanamycin-intermediate or -resistant E. coli isolate at a farm (OR=8.7 and 9.3, respectively). Use of systemically administered tetracycline and ceftiofur was associated with cefoxitin-intermediate or -resistant E. coli (OR=0.13 and 0.16, respectively); however, the odds of a dairy herd having at least 1 cefoxitin-intermediate or -resistant E. coli isolate due to systemically administered ceftiofur increased with increasing average herd parity (OR=3.1). Association between herd-level AMU and AMR in bovine mastitis coliforms was observed for certain antimicrobials. Differences in AMR between different barn types and geographical regions were not observed.

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 score0.101
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
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 head0.075
GPT teacher head0.265
Teacher spread0.190 · 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

Citations56
Published2013
Admission routes3
Has abstractyes

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