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Record W4387765916 · doi:10.3168/jds.2023-23937

Frequency of isolation and phenotypic antimicrobial resistance of fecal Salmonella enterica recovered from dairy cattle in Canada

2023· article· en· W4387765916 on OpenAlexafffundabout
Mariana Fonseca, Luke C. Heider, Henrik Stryhn, J. Trenton McClure, David Léger, Daniella Rizzo, Simon Dufour, Jean‐Philippe Roy, D.F. Kelton, D.L. Renaud, Herman W. Barkema, Javier Sánchez

Bibliographic record

VenueJournal of Dairy Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsUniversity of CalgaryUniversité de MontréalUniversity of GuelphCegep de Saint HyacinthePublic Health Agency of CanadaHealth PEIUniversity of Prince Edward Island
FundersAgriculture and Agri-Food CanadaPublic Health AgencyPublic Health Agency of CanadaDairy Farmers of Canada
KeywordsSalmonellaSalmonella entericaSerotypeVeterinary medicineFecesBiologyDairy cattleAntibiotic resistanceHerdMicrobiologyAnimal scienceMedicineAntibioticsBacteria

Abstract

fetched live from OpenAlex

Salmonellosis is one of the leading causes of gastrointestinal infections in humans. In Canada, it is estimated that approximately 87,500 cases of salmonellosis occur every year in humans, resulting in 17 deaths. In the United States, it is estimated that 26,500 hospitalizations and 420 deaths occur every year. In dairy cattle, infections caused by nontyphoidal Salmonella enterica can cause mild to severe disease, including enteritis, pneumonia, and septicemia. Our study objectives were to determine the proportion of fecal samples positive for Salmonella in dairy cattle in Canada and determine the resistance pattern of these isolates. We used data collected through the Canadian Dairy Network for Antimicrobial Stewardship and Resistance (CaDNetASR). Pooled fecal samples from preweaning calves, postweaning heifers, lactating cows, and manure storage were cultured for Salmonella, and the isolates were identified using matrix-assisted laser desorption/ionization-time of flight mass spectrometry. Antimicrobial susceptibilities were determined using the minimum inhibitory concentration test, and resistance interpretation was made according to the Clinical and Laboratory Standards Institute. A 2-level, multivariable logistic regression model was built to determine the probability of recovering Salmonella from a sample, accounting for province, year, and sample source. The proportion of farms with at least one positive sample were 12% (17/140), 19% (28/144), and 17% (24/144) for the sampling years 2019, 2020, and 2021, respectively. Out of the 113 Salmonella isolates, 23 different serovars were identified. The occurrence of Salmonella appeared to be clustered by farms and provinces. The most common serovars identified were Infantis (14%) and Typhimurium (14%). Overall, 21% (24/113) of the Salmonella isolates were resistant to at least one antimicrobial. Resistance to tetracycline was commonly observed (17%); however, very limited resistance to category I antimicrobials (categorization according to Health Canada that includes third-generation cephalosporins, fluoroquinolones, polymyxins, and carbapenems) was observed, with one isolate resistant to amoxicillin and clavulanic acid. The proportion of Salmonella isolates resistant to 2 and 3 antimicrobial classes was 3.5% and 8.8%, respectively. Our study provided valuable information on the proportion of fecal samples positive for Salmonella, the serovars identified, and the associated resistance patterns across CaDNetASR herds, at regional and national levels.

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 score0.034
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
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 head0.020
GPT teacher head0.228
Teacher spread0.208 · 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

Citations6
Published2023
Admission routes3
Has abstractyes

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