Antimicrobial Stewardship: A One Health approach with a focus on antimicrobial reduction in dairy cattle
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is considered one of the greatest threats facing humanity. Without intervention, AMR impacts are expected to be substantial, compromising human, animal, environmental health. The complex interplay of contributing factors highlights the need for a One Health approach in AMR mitigation. Improving antimicrobial stewardship (AMS) is an integral component of AMR mitigation success. Therefore, thesis objectives included: 1) describe the current state of AMR knowledge in Canada available in the literature, and identify the gaps in our understanding; 2) identify perspectives of AMS, including perceived drivers and barriers across the One Health spectrum of relevant Canadian professionals; 3) focus on the dairy industry as an example where AMS efforts are possible through selective dry cow therapy (SDCT); and 4) describe current SDCT uptake and related practices in the Canadian dairy industry. Current limitations in the understanding of AMR in Canada are described through a comprehensive review focussed on: 1) treatment optimization; 2) surveillance of antimicrobial use (AMU) and AMR; and 3) prevention of transmission of AMR. Whereas identified barriers to AMS described by Canadian professionals included: 1) lack of various prescribing and AMU support mechanisms; 2) shift in prescriber attitudes to drive change; and 3) stronger economic considerations to support shifting prescribing practices. Only treating cows who could benefit from antimicrobials at drying off (i.e., SDCT), represents an opportunity to reduce AMU in the dairy industry. A narrative review was conducted summarizing available literature regarding impacts of SDCT on udder health, milk production, economics, AMU motivations, and AMR. An observational study was conducted utilizing 2 in-person questionnaires between July 2019 and September 2021 on 144 dairy farms in 5 Canadian provinces. Overall, 31% reported adopting SDCT, with approximately 50% less intramammary AMU at drying off compared to treating all cows. A slight majority of farms (56%) applied teat sealants (TS) to all cows at drying off, whereas 12% used TS selectively, and 32% did not use TS. Results highlighted the variability in antimicrobial and TS use protocols at drying off on Canadian dairy farms, and the potential for further AMU reduction with increased SDCT adoption.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".