Indications for the use of highest priority critically important antimicrobials in the veterinary sector
Bibliographic record
Abstract
BACKGROUND: Among the measures taken to preserve the clinical efficacy of highest priority critically important antimicrobials (HP-CIAs), the WHO has recommended avoiding their use in food-producing animals. Little is known regarding the indications for which different antimicrobial classes are used in animals, even in countries where data on antimicrobial use are available. OBJECTIVES: To outline, in a narrative review, the diseases for which HP-CIAs are used in veterinary medicine, highlighting incongruences with international guidelines and disease conditions where effective alternatives to HP-CIAs are missing. METHODS: Scientific literature, national reports and expert opinion were used to describe the indications for the use of HP-CIAs in the main food-producing (pigs, cattle and poultry) and companion (horses, dogs and cats) animal species. RESULTS: The most common indications for use of HP-CIAs are enteric and respiratory infections in pigs, cattle and poultry, urogenital infections in dogs and cats and respiratory infections in horses. In some instances, no valid and convenient alternatives to colistin and macrolides are available against certain porcine enteric and bovine respiratory pathogens. Effective, legal and convenient alternatives to HP-CIAs are also lacking for managing common infections in cats, for which oral administration is difficult, Rhodococcus equi infections in horses, some enteric and respiratory infections in poultry and MDR infections in all companion animal species. CONCLUSIONS: Future research and stewardship programmes should focus on the disease conditions identified by this review to reduce the use of HP-CIAs in the veterinary 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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".