Quantification of the risk of Extended-Spectrum Beta-Lactamase producing Escherichia coli colonization in humans through occupational exposure in broiler production
Bibliographic record
Abstract
We propose a Quantitative Microbiological Risk Assessment (QMRA) model to quantify the risk of Extended-Spectrum Beta-Lactamase (ESBL) producing Escherichia coli (E. coli) colonization among humans through occupational exposure from broiler production. The contribution of this work is two-fold: Firstly, we adapt an existing QMRA model, originally proposed by Collineau et al. (2020) for Salmonella Heidelberg in the Canadian context, to assess the exposure to ESBL E. coli across various steps of the broiler farm-to-fork production chain within the European context. Secondly, we develop a novel QMRA model based on the guidelines of Codex Alimentarius Commission (2014) to quantify the transmission of ESBL E. coli to workers involved in the broiler production chain, via direct contact with contaminated surfaces and elements at different production steps. To the best of our knowledge, this is the first QMRA model to estimate the probability of colonization by ESBL E. coli among occupational groups engaged in various steps of broiler production. The model is used to identify steps with the highest occupational exposure and to evaluate the effectiveness of different hygiene interventions—including mask use, handwashing, and glove use—in reducing workers’ exposure. The proposed QMRA framework is designed as a risk assessment tool aligned with the One Health approach, and it is adaptable and scalable to specific broiler production systems according to the needs of risk managers. Additionally, this article discusses challenges in QMRA model validation, emphasizes the limitations of the proposed model, and explores future perspectives for improvement.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".