Cross-sectional study of antimicrobial use and treatment decision for preweaning Canadian dairy calves
Bibliographic record
Abstract
Antimicrobials should be used prudently in farm animals to prevent the development of resistant bacteria in both humans and animals. The objective of this study was to investigate Canadian dairy producers' practices for antimicrobial use in the treatment of disease in preweaning dairy calves. In-person questionnaires were administered to 144 dairy producers across 5 provinces in Canada between July 2019 and August 2020. Almost all (96%) producers used antimicrobials to treat calves with respiratory disease, but only 27% indicated they had a written treatment protocol for respiratory disease. Most (95%) of these protocols for respiratory disease were developed with input from the herd veterinarian. Seventy-four percent of producers used antimicrobials to treat calf diarrhea, with 37% of producers having a written treatment protocol for calf diarrhea with input from the herd veterinarian. The combinations of signs adopted by the producers for antimicrobial treatment in calf respiratory disease and diarrhea were evaluated based on findings from other studies. More than half (56%) of producers who used antimicrobials for calf respiratory disease decided to use antimicrobials by evaluating multiple clinical signs. Eighty-two percent of producers who used antimicrobials for calf diarrhea made decisions based on systemic signs of disease, presence of bloody stool, no response to previous treatment, or on the recommendation from the herd veterinarian. Producers with a written treatment protocol had 3 to 7 times greater odds of using antimicrobials based on multiple signs or systemic signs of disease compared with those without a protocol. Further research may investigate other calf management practices related to decision-making by producers in using antimicrobials to improve antimicrobial stewardship on dairy farms.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".