Monitoring Antimicrobial Drug Usage in Animals: Methods and Applications
Bibliographic record
Abstract
Monitoring antimicrobial drug usage in animals at the national and international levels is important for identification and tracking if and how often quantities are used. This information can be used for many purposes, including raising awareness, comparing use patterns across countries, identifying trends over time, integrating with antimicrobial resistance data, conducting risk assessment, and evaluating the effectiveness of measures to manage antimicrobial usage. The goal of this article is to describe how monitoring systems for antimicrobial drug usage in animals are set up and conducted, using examples from specific countries as well as international efforts. Several key figures and variables are used to describe and evaluate antimicrobial consumption in animals, including the amount in kilograms of active ingredient, standardized units (e.g., number of defined daily dose animals, DDDAs) and number of treatments (e.g., number of used daily doses, UDDA). Data can be collected from a variety of sources including pharmaceutical sales, pharmacy dispensing, veterinary prescriptions, and farm records. In many countries, data analysis and reporting at the national level provide statistics on overall quantities used in animals, in some cases by animal species. Antimicrobial consumption data should be contrasted to the respective animal population, for example, the weight of different categories of livestock and slaughtered animals. Several countries have established antimicrobial usage monitoring systems. Most report overall sales data, but some provide usage data to the levels of animal species and production type. At the international level, several organizations (e.g., European Union, World Organization for Animal Health, World Health Organization) have initiatives to support the development of antimicrobial consumption data collection and reporting. However, these initiatives are ongoing and so far lack harmonization, which will be the biggest challenge for the future.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".