Antimicrobial use and stewardship practices on Australian beef feedlots
Bibliographic record
Abstract
OBJECTIVE: Improving antimicrobial stewardship in the livestock sector requires an understanding of the motivations for antimicrobial use and the quantities consumed. However,detailed information on antimicrobial use in livestock sectors is lacking. This cross-sectional study aimed to better understand antimicrobial use in the beef feedlot sector in Australia. DESIGN: A self-administered questionnaire asking about antimicrobial use and reasons for use was designed and mailed to beef feedlot operators in Australia. Respondents were asked to report the percentage of animals treated, purpose of use, and disease conditions targeted for 26antimicrobial agents. RESULTS: In total, 83 of 517 (16.1%) beef feedlot operators completed the survey. Monensin (61.0%of respondents) and virginiamycin (19.5%of respondents) were the most commonly reported in-feed antimicrobials. In-feed antimicrobial agents were most frequently used by respondents for treatment of gastrointestinal diseases (52.8%). Antimicrobials were used for growth promotion by 42.1% of respondents, with most (85.7%) reporting the use of ionophores(a group of compounds not used in human medicine). Short-acting penicillin(69.1%), short-acting oxytetracycline, and tulathromycin (both 57.3%) werethe most common injectable antimicrobial agents used. Injectable antimicrobials were most frequently used to treat respiratory (72.3%) and musculoskeletal (67.5%) conditions. CONCLUSION: Overall,the use of antimicrobials was appropriate for the purpose indicated, and there was a strong preference for drugs of low-importance in human medicine. The data described here stand to be a strong influence on the implementation of an antimicrobial stewardship program in the sector.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".