Old problems and new solutions: antibiotic alternatives in food animal production
Bibliographic record
Abstract
The antimicrobial resistance crisis is a Global Health challenge that impacts humans, animals, and the environment alike. In response to increased demands for animal protein and by-products, there has been a substantial increase in the use of antimicrobial agents in the animal industry. Indeed, they are extensively used to prevent, control, and (or) treat disease in animals. In addition to infection control, in-feed supplementation with antimicrobials became common practice for growth promotion of livestock. Unfortunately, the global overuse of antimicrobials has contributed to the emergence and spread of resistance. As such, many countries have implemented policies and approaches to eliminate the use of antimicrobials as growth promoters in food animals, which necessitates the need for alternate and One Health strategies to maintain animal health and welfare. This review summarizes the antimicrobial resistance crisis from Global Health and One Health perspectives. In addition, we outline examples of potential alternate strategies to circumvent antimicrobial use in animal husbandry practices, including antivirulence agents, bacteriophages, and nutritional measures to control bacterial pathogens. Overall, these alternate strategies require further research and development efforts, including assessment of efficacy and the associated development, manufacturing, and labor costs.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".