Effect of Antimicrobial Use in Agricultural Animals on Drug-resistant Foodborne Campylobacteriosis in Humans: A Systematic Literature Review
Bibliographic record
Abstract
Controversy continues concerning antimicrobial use in food animals and its relationship to drug-resistant infections in humans. We systematically reviewed published literature for evidence of a relationship between antimicrobial use in agricultural animals and drug-resistant foodborne campylobacteriosis in humans. Based on publications from the United States (U.S.), Canada and Denmark from 2010 to July 2014, 195 articles were retained for abstract review, 50 met study criteria for full article review with 36 retained for which data are presented. Two publications reported increase in macrolide resistance of Campylobacter coli isolated from feces of swine receiving macrolides in feed, and one of these described similar findings for tetracyclines and fluoroquinolones. A study in growing turkeys demonstrated increased macrolide resistance associated with therapeutic dosing with Tylan® in drinking water. One publication linked tetracycline-resistant C. jejuni clone SA in raw cow's milk to a foodborne outbreak in humans. No studies that identified farm antimicrobial use also traced antimicrobial-resistant Campylobacter from farm to fork. Recent literature confirms that on farm antibiotic selection pressure can increase colonization of animals with drug-resistant Campylobacter spp. but is inadequately detailed to establish a causal relationship between use of antimicrobials in agricultural animals and prevalence of drug-resistant foodborne campylobacteriosis in humans.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".