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Record W7036130239

Antimicrobial use and resistance in Canadian cow-calf herds

2022· dissertation· en· W7036130239 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2022
Typedissertation
Languageen
FieldArts and Humanities
TopicAncient Mediterranean Archaeology and History
Canadian institutionsnot available
Fundersnot available
KeywordsHerdAntimicrobialLivestockOxytetracyclineAntibiotic resistanceIce calving
DOInot available

Abstract

fetched live from OpenAlex

Antimicrobial resistance (AMR) is a concern to human health and has been a growing concern for the public and livestock producers in recent years. The existing literature regarding antimicrobial use (AMU) and AMR in cow-calf herds, a critical component of the beef supply chain, is limited and much of it is more than a decade old. The goal of this thesis is to address the existing gaps by examining AMU practices on Canadian cow-calf operations and AMR in two enteric species important to human health: E. coli and Enterococcus.\nIn Chapter 2, a survey was used to collect AMU data from herds across the country. AMU data for the period of July 1, 2019, to June 30, 2020, was collected from 146 herds. Ninety-nine percent (145/146) reported the use of an antimicrobial at least once during the study period; however, frequency of use within herds was low. The antimicrobial most likely to be reported as used by participating herds was oxytetracycline (81%, 118/146). Category I antimicrobials were used at least once by 33% (48/146) of herds, with no herds reporting treatment of more than 30% of animals with a Category I antimicrobial. Factors such as calving season and herd type were shown to influence AMU practices. Overall, AMU practices were similar to previous studies examining AMU in cow-calf herds.\nIn Chapter 3, AMR patterns in fecal E. coli were examined in samples collected from cows and calves in the spring and fall of 2021 from western Canadian herds. In total, 1,551 E. coli isolates were obtained from 809 calves and 746 cows, resulting in an isolation rate of 99.7%. AMR susceptibility testing was completed using the NARMS panel for gram-negative bacteria. Overall, 15% (231/1551) of the recovered isolates were resistant to a single antimicrobial. Resistance was found at least once in nearly every herd (90%, 35/39). Resistance of E. coli to Category I antimicrobials was very infrequent, with tetracycline being the most common resistance target. Calves were more likely to display resistance than cows, with a higher proportion of calves also displaying multiclass resistance. Additionally, calves in the spring were more likely to display resistance compared to calves in the fall.\nIn the fourth chapter, antimicrobial resistance patterns were described for Enterococcus. Enterococcus has not previously been studied in Canadian cow-calf herds. Recovery rates for Enterococcus were good (97%), with 1,505 isolates recovered from 1,555 animals consisting of 809 calves and 746 cows. Resistance of isolates to at least one antimicrobial was 98% in the spring and 96% in the fall. The antimicrobials of quinupristin/dalfopristin and tetracycline were common resistance targets in both cows and calves. When summarized at the herd level, multiclass resistance and resistance to Category I antimicrobials was greater in calves than in cows. AMR resistance in Enterococcus is complicated by questions regarding the role of intrinsic resistance in observed susceptibility data. There are also concerns that current minimum inhibitory concentration (MIC) breakpoints are not accurate for all Enterococcal species further complicating the interpretation of the study findings. Future studies will be required to better understand the prevalence of resistance amongst different bacteria of interest in cow-calf herds.

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 score0.031
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.156
Teacher spread0.145 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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