Factors potentially linked with the occurrence of antimicrobial resistance in selected bacteria from cattle, chickens and pigs: A scoping review of publications for use in modelling of antimicrobial resistance (IAM.AMR Project)
Bibliographic record
Abstract
Antimicrobial resistance is a complex issue with a large volume of published literature, and there is a need for synthesis of primary studies for an integrated understanding of this topic. Our research team aimed to have a more complete understanding of antimicrobial resistance in Canada (IAM.AMR Project) using multiple methods including the literature reviews and quantitative modelling. To accomplish this goal, qualitative features of publications (e.g., geographical location, study population) describing potential relationships between the occurrence of antimicrobial resistance and factors (e.g., antimicrobial use; management system) were of particular interest. The objectives of this review were to (a) describe the available peer-reviewed literature reporting potential relationships between factors and antimicrobial resistance; and (b) to highlight data gaps. A comprehensive literature search and screening were performed to identify studies investigating factors potentially linked with antimicrobial resistance in Campylobacter species, Escherichia coli and Salmonella enterica along the farm-to-fork pathway (farm, abattoir (slaughter houses) and retail meats) for the major Canadian livestock species (beef cattle, broiler chicken and pigs). The literature search returned 14,966 potentially relevant titles and abstracts. Following screening of titles, abstracts and full-text articles, the qualitative features of retained studies (n = 28) were extracted. The most common factors identified were antimicrobial use (n = 13 studies) and type of farm management system (e.g., antibiotic-free, organic; n = 8). Most studies were conducted outside of Canada and involved investigations at the farm level. Identified data gaps included the effect of vaccination, industry-specific factors (e.g., livestock density) and factors at sites other than farm along the agri-food chain. Further investigation of these factors and other relevant industry activities are needed for the development of quantitative models that aim to identify effective interventions to mitigate the occurrence of antimicrobial resistance along the agri-food chain.
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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 | 0.029 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.043 | 0.045 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".