Outbreaks of Escherichia coli O157:H7 Infections Linked to Romaine Lettuce in Canada from 2008 to 2018: An Analysis of Food Safety Context
Bibliographic record
Abstract
ABSTRACT: Foodborne diseases are a major cause of illness in Canada. One of the main pathogens causing cases and outbreaks of foodborne illness in Canada is Escherichia coli O157:H7. From 2008 to 2018, 11 outbreaks of E. coli O157:H7 infection in Canada were linked to leafy greens, including 7 (63.6%) linked to romaine lettuce, 2 (18.2%) linked to iceberg lettuce, and 2 (18.2%) linked to other or unspecified types of leafy greens. The consumption of lettuce in Canada, the behavior of E. coli O157:H7 on lettuce leaves, and the production practices used for romaine and iceberg lettuce do not seem to explain why a higher number of outbreaks of E. coli O157:H7 infection were linked to romaine than to iceberg lettuce. However, the difference in the shape of iceberg and romaine lettuce heads could be an important factor. Among the seven outbreaks linked to romaine lettuce in Canada between 2008 and 2018, an eastern distribution of cases was observed. Cases from western provinces were reported only twice. The consumption of romaine and iceberg lettuce by the Canadian population does not seem to explain the eastern distribution of cases observed, but the commercial distribution, travel distances, and the storage practices used for lettuce may be important factors. In the past 10 years, the majority of the outbreaks of E. coli O157:H7 infection linked to romaine lettuce occurred during the spring (March to June) and fall (September to December). The timing of these outbreaks may be explained by the availability of lettuce in Canada, the growing region transition periods in the United States, and the seasonality in the prevalence of E. coli O157:H7. The consumption of romaine lettuce by the Canadian population does not explain the timing of the outbreaks observed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".