The Impact Of Antimicrobial Use In Veterinary Medicine On Resistance Development
Bibliographic record
Abstract
The use of antimicrobial in veterinary medicine is a double edged sword: while it is essential for the health and welfare of animals, it also raises the risk of antimicrobial resistance (AMR). The impact of antimicrobial use in veterinary care on the emergence and spread of resistance is thoroughly examined in this review. It sheds light on the different aspects of antimicrobial resistance (AMR), such as the kind and frequency of antibiotic use, livestock management techniques, and the relationships between human and animal health. This review focuses on the genetic pathways that transmit resistance, highlighting the mechanisms by which drug-resistant bacteria proliferate and emerge. It also looks at the effects of veterinary antimicrobial use on public health, specifically the possibility of zoonotic transmission of pathogens resistant to drugs. This review offers a critical evaluation of the management initiatives and regulatory frameworks currently in place to mitigate antimicrobial resistance (AMR), as well as strategies for sustained antimicrobial use. The attempts to provide an in-depth awareness of the complexity of veterinary antimicrobial use and its crucial role in influencing the global antimicrobial resistance landscape by synthesizing recent research findings.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".