Molecular epidemiology of antimicrobial resistance in veterinary medicine: where do we go?
Bibliographic record
Abstract
Molecular epidemiology allows us to trace specific microorganisms and mobile genetic elements and to assess their epidemiological and evolutionary relationships. Examples of molecular epidemiology investigations in veterinary hospitals are discussed. They demonstrate the great similarities with the situation in human medicine and the potential usefulness of molecular epidemiology in our fight against antimicrobial resistance and nosocomial infections in veterinary hospitals. A broad knowledge of the diversity of antimicrobial resistance determinants in some major groups of pathogens and commensals from animals such as Enterobacteriaceae, Pasteurellaceae, enterococci and staphylococci is emerging. However, there are important gaps in this knowledge, which are discussed here. Many more molecular epidemiology studies will be necessary to understand and follow the evolution of the problem in veterinary medicine and agriculture on a global scale. To be able to build useful surveillance programs and reliable epidemiological models, and to identify critical intervention points, we need to improve our understanding of antimicrobial resistance at the animal and farm levels. Studies assessing the dynamics of bacterial populations and of resistance determinants at these levels are desperately needed. Understanding the relationships between antimicrobial resistance, colonization factors, and virulence also represents a major issue for which molecular epidemiology investigations will be needed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".