Monitoring of Antimicrobial Resistance in Animals: Principles and Practices
Bibliographic record
Abstract
This chapter reviews information relevant to the design and scope of antimicrobial resistance monitoring and surveillance programs for animals and food, with emphasis on program purposes and methods. The chapter describes some of the essential features of existing monitoring and surveillance programs in various countries around the world. It shows how these programs have been useful in improving understanding of resistance and its relation to antimicrobial use and other factors, guiding public policy, and measuring the impact of interventions on antimicrobial resistance in bacteria from animals, food, and humans. The major methodological considerations for the monitoring program include the types of samples to be collected, sampling strategies, species of bacteria, antimicrobials for susceptibility testing, data collection and analysis, and reporting of results. Comprehensive monitoring of antimicrobial resistance in animals in the context of animal and human health covers the entire farm-to-fork continuum. The Food and Drug Administration (FDA) Center for Veterinary Medicine (CVM) has been active in developing new approaches for the preapproval assessment of antimicrobial resistance risks from antimicrobials used in animals. The Japanese Veterinary Antimicrobial Resistance Monitoring (JVARM) program examines the susceptibility of bacteria from food-producing animals to antimicrobial agents. Most programs focus on pathogenic bacteria or Salmonella, but some also report data on resistance in indicator bacteria isolated from healthy animals. Knowledge about antimicrobial resistance should be combined with knowledge regarding the usage of antimicrobial agents for different food animal species, which also should be performed on an internationally comparable basis.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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".