The Prevalence of <i>Campylobacter</i> in Live Cattle, Turkey, Chicken, and Swine in the United States and Canada: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Campylobacter cause gastroenteritis in humans and may be shed in the feces of livestock and poultry species, including cattle, chicken, turkey, and swine. However, a synthesis of the prevalence on farms in the United States and Canada is currently lacking. Thus, our objective was to conduct a systematic review and meta-analysis to estimate the prevalence of Campylobacter coli , Campylobacter jejuni , and Campylobacter spp. on livestock and poultry farms operated under commercial conditions in the United States and Canada. The relevant literature was identified and examined for eligibility based on a priori inclusion and exclusion criteria. Relevant data were extracted, and a meta-analysis was performed. The data were transformed using the Freeman-Tukey arcsine transformation to stabilize the variance. A separate meta-analysis was performed for each animal species, level of sampling (individual versus pooled), and species of Campylobacter , for a total of 29 meta-analyses. C. jejuni and Campylobacter spp. were present in all livestock and poultry species of interest, whereas C. coli was found in all species of interest with the exception of chickens. Furthermore, substantial heterogeneity was observed in most meta-analyses. In an attempt to account for this, subgroup analyses were performed on potential moderators. However, with the exception of beef cattle, where studies in feedlot cattle reported a consistently higher prevalence compared with adult cattle on pasture, significant heterogeneity remained in the majority of meta-analyses after accounting for potential moderators. The results of this review can be used to inform future risk assessment, consumer and producer awareness, and resource allocation, and identify gaps for future research.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.036 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".