Two Listeria monocytogenes outbreaks in a cancer centre: onsite food premises and their potential health risk to patients
Bibliographic record
Abstract
BACKGROUND: This report describes two L. monocytogenes outbreak investigations that occurred in March and September of 2018 and that linked illness to a food premises located in an Ontario cancer centre. The cancer centre serves patients from across the province. METHODS: In Ontario, local public health agencies follow up with all reported laboratory-confirmed cases of listeriosis to identify possible sources of disease acquisition and to carry out investigations, including at suspected food premises. The Canadian Food Inspection Agency (CFIA) is notified of any Listeria-positive food product collected in relation to a case. The CFIA traces Listeria-positive product through the food distribution system to identify the contamination source and ensure the implicated manufacturing facility implements corrective measures. RESULTS: Outbreaks one and two each involved three outbreak-confirmed listeriosis cases. All six cases were considered genetically related by whole genome sequencing (WGS). In both outbreaks, outbreak-confirmed cases reported consuming meals at a food premises located in a cancer centre (food premises A) before illness onset. Various open deli meat samples and, in outbreak two, environmental swabs (primarily from the meat slicer) collected from food premises A were genetically related to the outbreak-confirmed cases. Food premises A closed as a result of the investigations. CONCLUSIONS: When procuring on-site food premises, healthcare facilities and institutions serving individuals with immuno-compromising conditions should consider the potential health risk of foods available to their patient population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".