Verotoxigenic Escherichia coli (VTEC): A major public health threat in Canada
Bibliographic record
Abstract
BACKGROUND: Verotoxigenic Escherichia coli (VTEC) was first described in Canada during the 1980s as an emerging foodborne disease in association with morbidity and mortality in outbreaks of hemorrhagic colitis caused by E coli O157:H7. OBJECTIVE: To describe the surveillance activities and epidemiological laboratory markers of VTEC that are used at the National Laboratory for Enteric Pathogens (NLEP) to investigate sporadic cases and outbreaks of E coli O157:H7 and non-O157 VTEC in Canada. METHODS: Passive surveillance was conducted by obtaining data on laboratory confirmed cases of VTEC from the Provincial Laboratories of Public Health across Canada. The laboratory epidemiological markers generated for isolates of VTEC included biotyping, serotyping, phage typing, toxin detection and characterization, and molecular typing using pulsed-field gel electrophoresis. RESULTS: Major outbreaks of VTEC O157:H7 disease have been associated with ground beef, unpasteurized apple juice, salami and untreated water. In 1999 and 2000, a total of 46 outbreaks of E coli O157:H7 disease were investigated. Among those, one outbreak was associated with contact at a petting zoo and a second with the consumption of salami. An outbreak in 2000 in Ontario was associated with water and resulted in more than 1000 cases of human illness, with six deaths. The NLEP has also identified more than 100 non-O157 VTEC serotypes from cattle and meat products. At least 23 VTEC serotypes found in humans were also identical to those found in cattle and meat products. CONCLUSIONS: The laboratory-based information that is generated is used to define the incidence, sources of infection, risk factors, trends, distribution and transmission of VTEC to humans from food, water and animal sources. Prevention and control of outbreaks are high-priority health concerns.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".