Optimization of Antimicrobial Treatment to Minimize Resistance Selection
Bibliographic record
Abstract
Optimization of antimicrobial treatment is a cornerstone in the fight against antimicrobial resistance. Various national and international authorities and professional veterinary and farming associations have released generic guidelines on prudent antimicrobial use in animals. However, these generic guidelines need to be translated into a set of animal species- and disease-specific practice recommendations. This article focuses on prevention of antimicrobial resistance and its complex relationship with treatment efficacy, highlighting key situations where the current antimicrobial drug products, treatment recommendations, and practices may be insufficient to minimize antimicrobial selection. The authors address this topic using a multidisciplinary approach involving microbiology, pharmacology, clinical medicine, and animal husbandry. In the first part of the article, we define four key targets for implementing the concept of optimal antimicrobial treatment in veterinary practice: (i) reduction of overall antimicrobial consumption, (ii) improved use of diagnostic testing, (iii) prudent use of second-line, critically important antimicrobials, and (iv) optimization of dosage regimens. In the second part, we provided practice recommendations for achieving these four targets, with reference to specific conditions that account for most antimicrobial use in pigs (intestinal and respiratory disease), cattle (respiratory disease and mastitis), dogs and cats (skin, intestinal, genitourinary, and respiratory disease), and horses (upper respiratory disease, neonatal foal care, and surgical infections). Lastly, we present perspectives on the education and research needs for improving antimicrobial use in the future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".