Companions in antimicrobial resistance: examining transmission of common antimicrobial-resistant organisms between people and their dogs, cats, and horses
Bibliographic record
Abstract
SUMMARYNumerous questions persist regarding the role of companion animals as potential reservoirs of antimicrobial-resistant organisms that can infect humans. While relative antimicrobial usage in companion animals is lower than that in humans, certain antimicrobial-resistant pathogens have comparable colonization rates in companion animals and their human counterparts, which inevitably raises questions regarding potential antimicrobial resistance (AMR) transmission. Furthermore, the close contact between pets and their owners, as well as pets, veterinary professionals, and the veterinary clinic environment, provides ample opportunity for zoonotic transmission of antimicrobial-resistant pathogens. Here we summarize what is known about the transmission of AMR and select antimicrobial-resistant organisms between companion animals (primarily dogs, cats, and horses) and humans. We also describe the global distribution of selected antimicrobial-resistant organisms in companion animals. The impact of interspecies AMR transmission within households and veterinary care settings is critically reviewed and discussed in the context of methicillin-resistant staphylococci, extended-spectrum β-lactamase and carbapenemase-producing bacteria. Key research areas are emphasized within established global action plans on AMR, offering valuable insights for shaping future research and surveillance initiatives.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".