A scoping review of factors potentially linked with antimicrobial-resistant bacteria from turkeys (iAM.AMR Project)
Bibliographic record
Abstract
Abstract Antimicrobial resistance (AMR) is a complex problem that is a threat to global public health. Consumption of turkey meat may be an important source of foodborne exposure to resistant bacteria; recent outbreaks of multi-drug-resistant Salmonella Reading in Canada and the USA have implicated raw turkey products. To better understand the epidemiology of AMR in farmed turkey production, a scoping review was conducted. The objectives were to identify (1) modifiable factors potentially associated with antimicrobial-resistant Campylobacter, Enterococcus, Escherichia coli and Salmonella enterica along the farm-to-fork pathway in turkeys, and (2) data gaps with respect to factors potentially associated with AMR and Canadian commercial turkey production. A comprehensive search of the peer-reviewed literature was conducted in 2019 and updated in 2021. Thirteen references were included, reporting 36 factors. Antimicrobial use factors and their potential associations with AMR were most frequently reported (n = 15 factors; 42%), followed by biosecurity (n = 11; 31%) and management practices (n = 10; 28%). This review revealed important data gaps; no factors pertaining to S. enterica or to stages other than the farm (e.g. abattoir, retail) were identified, and only one Canadian reference was identified. These findings will inform priorities for future research and surveillance regarding turkeys and AMR.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.026 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".