Antimicrobial resistance associated with the use of antimicrobial processing aids during poultry processing operations: cause for concern?
Bibliographic record
Abstract
Antimicrobial resistance has become a global issue and a threat to human and animal health. Contamination of poultry carcasses with meat-borne pathogens represents both an economic and a public health concern. The use of antimicrobial processing aids (APA) during poultry processing has contributed to an improvement in the microbiological quality of poultry carcasses. However, the extensive use of these decontaminants has raised concerns about their possible role in the co-selection of antibiotic-resistant bacteria. This topic is presented in the current review to provide an update on the information related to bacterial adaptation to APA used in poultry processing establishments, and to discuss the relationship between APA bacterial adaptation and the acquisition of a new resistance phenotype to therapeutic antimicrobials by bacteria. Common mechanisms such as active efflux and changes in membrane fluidity are the most documented mechanisms responsible for bacterial cross-resistance to APA and antimicrobials. Although most studies reported a bacterial resistance to antibiotics not reaching a clinical level, the under-exposure of bacteria to APA remains a concern in the poultry industry. Further research is needed to determine if APA used during poultry processing and therapeutic antimicrobials share common sites of action in bacteria and encounter similar mechanisms of resistance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".