Antimicrobial Resistance and Food Animals: Influence of Livestock Environment on the Emergence and Dissemination of Antimicrobial Resistance
Bibliographic record
Abstract
The emergence and dissemination of antimicrobial resistance among human, animal and zoonotic pathogens pose an enormous threat to human health worldwide. The use of antibiotics in human and veterinary medicine, and especially the use of large quantities of antibiotics in livestock for the purpose of growth promotion of food animals is believed to be contributing to the modern trend of the emergence and spread of bacteria with antibiotic resistant traits. To better control the emergence and spread of antimicrobial resistance several countries from Western Europe implemented a ban for antibiotic use in livestock, specifically the use of antibiotics for growth promotion of food animals. This review article summarizes the recent knowledge of molecular acquisition of antimicrobial resistance and the effects of implementation of antibiotic growth promoter bans on the spread of antimicrobial resistant bacteria in animals and humans. In this article, we also discuss the main zoonotic transmission routes of antimicrobial resistance and novel approaches designed to prevent or slow down the emergence and spread of antimicrobial resistance worldwide. Finally, we provide future perspectives associated with the control and management of the emergence and spread of antimicrobial resistant bacteria.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".