Prevalence and Risk Factors for the Contamination of Cattle Carcasses With Shiga Toxin‐Producing <scp><i>Escherichia coli</i></scp> in Provincially Licensed Abattoirs in Ontario, Canada, Based on Molecular Surveillance
Bibliographic record
Abstract
INTRODUCTION: Reducing the prevalence of Shiga toxin-producing Escherichia coli (STEC) is an important responsibility of provincial abattoirs to ensure safe products are entering the human food chain. Currently, within Ontario, provincial abattoirs are mandated to apply various antimicrobial treatments to cattle carcasses to help decrease pathogen presence post-slaughter. The objectives of this study were to determine the prevalence of contamination of O157 and non-O157 STEC in carcasses from Ontario provincial abattoirs. METHODS: Using mixed logistic regression models, we examined the associations between cattle characteristics, season, monitoring program and abattoir interventions on carcass contamination with E. coli O157:H7, non-O157:H7 STEC and the top six non-O157:H7 STEC of concern to public health (i.e., O26, O45, O103, O111, O121 and O145). Random effects for abattoir and the area in which an abattoir was located were included in these models to adjust for clustering at these levels. The STEC examined was detected through two provincial molecular-based monitoring programs. RESULTS: Samples taken in the summer had significantly greater odds of screening positive for the top six STEC compared to samples taken in the fall and winter months. Similar seasonal effects were observed for E. coli O157:H7, but for only one of the monitoring programs (i.e., seasonal effects were modified by a monitoring program). Carcasses that received dry age treatment had significantly lower odds of screening positive for STEC. Samples collected from veal calf and cow carcasses had significantly greater odds of screening positive for STEC than samples taken from the carcasses of steers or heifers, but not bulls. Most of the variance in carcass contamination was explained at the carcass level. CONCLUSIONS: These results suggest that additional efforts in risk mitigation should focus on cattle of certain demographic characteristics and higher risk seasons and that additional carcass-level interventions be explored.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".