Developing Canadian Defined Daily Doses for Animals: A Metric to Quantify Antimicrobial Use
Bibliographic record
Abstract
Antimicrobial use surveillance data need to be analyzed and reported in a standardized and harmonized way. In veterinary medicine, one approach is to use defined daily doses (DDD) for animals. DDD for animals are technical standards used in various measures or metrics that quantify antimicrobial use. The European Medicines Agency published principles for assigning DDDvet values based on information on dosing obtained from nine European countries. For measuring antimicrobial use in livestock within Canada, DDDs for animals reflective of Canadian veterinary antimicrobial use (DDDvetCAs) were needed. Our objectives were (1) to describe the development of DDDvetCA standards for pigs and poultry (broiler chickens and turkeys) for authorized and compounded antimicrobial active ingredients used in Canada, including those used extra-label; and (2) to compare the DDDvetCAs with EMA's DDDvets, where possible. Species-specific DDDvetCAs were assigned based on the average of unique antimicrobial daily doses obtained from product information, stratified by route of administration and age indication (where applicable). The feed, water and bolus DDDvetCAs were compared to oral DDDvets, and injectable DDDvetCAs to parenteral DDDvets, that matched by antimicrobial active ingredient. Seventy-five DDDvetCAs were assigned for pigs; 51 for poultry. Seventeen injectable DDDvetCAs could be compared to 14 EMA's parenteral DDDvets and 53 feed, water, and bolus DDDvetCAs could be compared to 40 oral DDDvets. Feed and water DDDvetCAs were generally lower than EMA's oral DDDvets, although differences in methodology between Canada and Europe make comparisons challenging. The assignment of DDDvetCAs was a resource intensive and iterative process. EMA's published principles for assigning DDDvets were an invaluable source of information. The use of DDDvetCAs will reflect exposure of Canadian animals to antimicrobials, be useful for evaluating associations between use and resistance within Canada and provide information for risk assessment and stewardship policies. However, when reporting antimicrobial use data internationally, using the same DDD standards as other reporting countries will facilitate between country comparisons, although differences in which antimicrobial active ingredients are licensed between countries may create challenges. Future steps include assigning DDDvetCAs for other food animal species, such as cattle, veal, and farmed fish.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".