Monitoring of Antimicrobial Resistance in Animals: Principles and Practices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it