Low frequency of antimicrobial use in nursing beef calves from Western Canada has limited association with antimicrobial resistance in fecal organisms
Bibliographic record
Abstract
Objective: To examined potential risk factors for the frequency of antimicrobial use (AMU) within herd and potential associations between AMU and the frequency of antimicrobial resistance (AMR) in fecal organisms in beef calves. Methods: In this observational study, all interested herds from the Canadian Cow-Calf Surveillance Network shared individual treatment records in 2021 for nursing calves (N = 56); a subset also provided fecal samples from calves in the spring and fall of 2021 (N = 31). Herd attributes were examined for associations with frequency of AMU. Associations were also estimated between frequency of AMU by drug class and subsequent frequency of AMR for Escherichia coli and Enterococcus spp in the spring and fall. Results: Individual animal AMU for nursing calves included oxytetracycline, florfenicol, and macrolides. Herds with at least 300 versus < 300 cows, that sold some seedstock compared to 100% commercial, and assisted more cows at calving reported more frequent AMU within herd. Antimicrobial use drug class and AMR class were not associated with E coli or Enterococcus spp recovered from calf fecal samples in the spring or fall of 2021, except for macrolide use and macrolide resistance in the fall for Enterococcus spp (OR, 1.54; 95% CI, 1.26 to 1.89 per 10% increase). Conclusions: The frequency of AMU in nursing calves is relatively low in most herds. Antimicrobial use was not associated with AMR, with 1 exception. The availability of individual records and fecal samples limited statistical power. Clinical Relevance: Antimicrobial stewardship, including best practices for prescribing and administering antimicrobials, remains a priority for veterinarians and the beef industry.
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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 | 0.000 |
| 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".