Antimicrobial Resistance in Selected Enterobacteriaceae from Broilers and Their Environment: ESBL, AmpC, Carbapenemases, Colistin, and Fluoroquinolone Resistance—A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background/Objectives: Antimicrobial resistance (AMR) threatens global public health. This systematic review and meta-analysis, as part of the “ENVIRE” project (interventions to control the dynamics of antimicrobial resistance from chickens through the environment), assesses the prevalence of phenotypic and genotypic resistance, including extended-spectrum beta-lactamases (ESBLs), AmpC beta-lactamases, carbapenemases, colistin, and fluoroquinolone resistance, in broiler chickens and their environment. Methods: The analysis covers the years 2002–2022, focusing on Escherichia (E.) coli, Klebsiella spp., Enterobacter spp., and Citrobacter spp. in fecal, meat, environmental, and other-than-feces samples from observational studies published in PubMed and Web of Science. Quality assessment was performed using the Alberta Heritage Foundation criteria. Results: Data from 170 studies, conducted in Europe, North Africa, and North America, were included. The most frequently studied resistance was to beta-lactam, with focus on ESBL-producing and AmpC beta-lactamase isolates. The pooled prevalence of ESBL-resistant E. coli observed in meat samples at 41% and in fecal samples at 38% demonstrated significant heterogeneity between the studies. The negative binomial regression analysis of prevalence data revealed significantly higher ESBL-producing E. coli rates in European meat samples compared to North African samples. Conclusions: This systematic review revealed substantial variation in prevalence and emphasizes the need for standardized surveillance systems and robust study designs.
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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.013 | 0.011 |
| 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) | 0.002 | 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".