Antimicrobial Use in Canadian Cow–Calf Herds
Bibliographic record
Abstract
Despite growing concern surrounding antimicrobial use (AMU) and the importance of cow–calf herds to the Canadian livestock industry, surveillance of AMU in cow–calf herds to inform antimicrobial stewardship programs has been sporadic. Producers from the Canadian Cow–Calf Surveillance Network (87%, 146/168) provided data and almost all reported AMU in at least one animal (99%, 145/146 herds) in 2019–2020. The most common reasons for AMU were treatment of respiratory disease in nursing calves in 78% of herds and neonatal diarrhea in 67% of herds, as well as for lameness in cows in 83% of herds. However, most herds treated <5% of animals for these reasons. Less than 2.5% of herds treated more than 30% of calves for either bovine respiratory disease or neonatal diarrhea and no herds treated more than 30% of cows for lameness. The most frequently reported antimicrobial was oxytetracycline in 81% of herds, followed by florfenicol in 73% of herds. Antimicrobials with very high importance to human health, such as ceftiofur, were used at least once by 20% of herds but were only used in >30% of nursing calves from one herd. Similarly, while 56% of herds used macrolides at least once, within-herd use was the highest in nursing calves where <4% of herds reported use in >30% of animals. Herds using artificial insemination and calving in the winter were more likely (p = 0.05) to treat >5% of nursing calves for respiratory disease, suggesting the importance of vaccination programs for herds at risk. Overall, AMU was similar to previous Canadian studies; however, the percentage of herds using macrolides had increased from a comparable study in 2014.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".