A Scoping Review of Antimicrobial Usage and Antimicrobial Resistance in Beef Cow–Calf Herds in the United States and Canada
Bibliographic record
Abstract
Background: The magnitude and knowledge gaps regarding antimicrobial use (AMU) and antimicrobial resistance (AMR) have not been summarized for the North American cow–calf production sector, although estimates of AMU and AMR are essential to AMR risk analysis. The objectives of this scoping review were to map AMU and AMR in the beef cow–calf sector in Canada and the United States, summarize published AMU/AMR predictors, and identify research gaps. Methods: An electronic search was conducted of four bibliographic databases and Google Scholar, augmented by a hand-search of captured studies. Results: Twenty-three of three-hundred and forty-three publications screened advanced to data extraction. Of these, 10 were conducted in the USA and 13 in Canada. Thirteen studied AMR and twelve studied AMU, with two reporting both. Of twelve captured AMU studies, nine presented counts of herd AMU by antimicrobial class or specific antimicrobial. Antimicrobial resistance in Escherichia coli (E. coli) was reported in nine studies. Risk factors for AMU include herd size, vaccine use, and start date of calving season. Conclusions: Overall, a small number of AMR studies were available for synthesis in primarily one population (cows) reporting E. coli AMR. Additional studies targeting reasons for AMU in calves, the impact of management procedures on AMU, potential environmental AMR sources, and AMR in respiratory pathogens and enteric organisms other than E. coli for pre-weaning calves are required to inform AMR risk mitigation strategies.
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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 | 0.015 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.052 | 0.062 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".