Production systems and important antimicrobial resistant-pathogenic bacteria in poultry: a review
Bibliographic record
Abstract
Economic losses and market constraints caused by bacterial diseases such as colibacillosis due to avian pathogenic Escherichia coli and necrotic enteritis due to Clostridium perfringens remain major problems for poultry producers, despite substantial efforts in prevention and control. Antibiotics have been used not only for the treatment and prevention of such diseases, but also for growth promotion. Consequently, these practices have been linked to the selection and spread of antimicrobial resistant bacteria which constitute a significant global threat to humans, animals, and the environment. To break down the antimicrobial resistance (AMR), poultry producers are restricting the antimicrobial use (AMU) while adopting the antibiotic-free (ABF) and organic production practices to satisfy consumers' demands. However, it is not well understood how ABF and organic poultry production practices influence AMR profiles in the poultry gut microbiome. Various Gram-negative (Salmonella enterica serovars, Campylobacter jejuni/coli, E. coli) and Gram-positive (Enterococcus spp., Staphylococcus spp. and C. perfringens) bacteria harboring multiple AMR determinants have been reported in poultry including organically- and ABF-raised chickens. In this review, we discussed major poultry production systems (conventional, ABF and organic) and their impacts on AMR in some potential pathogenic Gram-negative and Gram-positive bacteria which could allow identifying issues and opportunities to develop efficient and safe production practices in controlling pathogens.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".