Antimicrobial and teat sealant use and selection criteria at dry-off on Canadian dairy farms
Bibliographic record
Abstract
Infections with antimicrobial resistant pathogens are a major threat to human and animal health worldwide. Further, reduction of livestock-associated antimicrobial use (AMU) is often identified as an area of focus. Selective dry cow therapy (DCT) warrants consideration as an important way to decrease AMU on Canadian dairy farms. In addition, teat sealants (TS) are a nonantimicrobial alternative for prevention of intramammary infection during the dry period. Therefore, objectives of this study were to determine how antimicrobials and TS are used at dry-off on Canadian dairy farms to determine selective DCT uptake and enacted selection protocols. It was expected that these data will provide a baseline understanding of DCT practices and highlight areas for future intervention to further reduce AMU. An observational study was conducted utilizing 2 in-person questionnaires conducted between July 2019 and September 2021 on 144 participating dairy farms in 5 Canadian provinces (British Columbia = 30, Alberta = 30, Ontario = 31, Québec = 29, and Nova Scotia = 24). Overall, 45 farms (31%) reported adopting selective DCT, 95 (66%) enacted blanket DCT, and 4 (3%) did not provide antimicrobial DCT. Farms enacting selective DCT had approximately 50% less intramammary antimicrobials used at dry-off compared with blanket DCT farms. Cow somatic cell count history was the most common criterion for selective DCT decision-making, followed by previous clinical mastitis history, bacteriological culture, and milk production. A slight majority of farms (56%) applied TS to all cows at dry-off, whereas 17 farms (12%) used TS selectively, and 46 farms (32%) did not use TS. Larger herds more often used TS, and farms with an automatic milking system more often used TS selectively than applied to all cows. Results highlighted the variability in antimicrobial treatment and TS use protocols at dry-off on Canadian dairy farms, and the potential for further antimicrobial reduction with increased adoption of selective DCT.
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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 | 0.002 | 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) | 0.001 | 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".