Antimicrobial use and stewardship practices on Australian beef feedlots
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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 it