World Health Organization (WHO) guidelines on use of medically important antimicrobials in food-producing animals
Bibliographic record
Abstract
Background: Antimicrobial use in food-producing animals selects for antimicrobial resistance that can be transmitted to humans via food or other transmission routes. The World Health Organization (WHO) in 2005 ranked the medical importance of antimicrobials used in humans. In late 2017, to preserve the effectiveness of medically important antimicrobials for humans, WHO released guidelines on use of antimicrobials in food-producing animals that incorporated the latest WHO rankings. Methods: WHO commissioned systematic reviews and literature reviews, and convened a Guideline Development Group (GDG) of external experts free of unacceptable conflicts-of-interest. The GDG assessed the evidence using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach, and formulated recommendations using a structured evidence-to-decision approach that considered the balance of benefits and harms, feasibility, resource implications, and impact on equity. The resulting guidelines were peer-reviewed by an independent External Review Group and approved by the WHO Guidelines Review Committee. Results: These guidelines recommend reductions in the overall use of medically important antimicrobials in food-producing animals, including complete restriction of use of antimicrobials for growth promotion and for disease prevention (i.e., in healthy animals considered at risk of infection). These guidelines also recommend that antimicrobials identified as critically important for humans not be used in food-producing animals for treatment or disease control unless susceptibility testing demonstrates the drug to be the only treatment option. Conclusions: To preserve the effectiveness of medically important antimicrobials, veterinarians, farmers, regulatory agencies, and all other stakeholders are urged to adopt these recommendations and work towards implementation of these guidelines.
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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 | 0.063 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".