A Binational Sample-Initiated Retrospective Outbreak Investigation of Listeria monocytogenes Infections in the United States and Canada Linked to Enoki Mushrooms Imported from China 2022–2023
Bibliographic record
Abstract
• Describes a binational outbreak of listeriosis linked to imported enoki mushrooms. • Emphasizes the importance of surveillance sampling in outbreak investigations. • Traceback, laboratory, and epidemiologic data helped identify the outbreak source. • Highlights the need for further research on enoki mushroom consumption patterns. In 2022, the U.S. Food and Drug Administration (FDA), the Centers for Disease Control and Prevention (CDC), U.S. state and local partners, the Public Health Agency of Canada (PHAC), and the Canadian Food Inspection Agency (CFIA) conducted a binational sample-initiated retrospective outbreak investigation (SIROI) of Listeria monocytogenes illnesses linked to enoki mushrooms. The FDA and CDC investigated the first known L. monocytogenes outbreak linked to enoki mushrooms from 2016 to 2020, making the 2022 outbreak the second time this pathogen-commodity pair was investigated by FDA and CDC. The 2022 outbreak included six ill people, all of whom were hospitalized. Epidemiologic, laboratory, and traceback evidence led to multiple public health actions, including voluntary recalls by firms, public communications about the outbreak, and FDA’s country-wide Import Alert for enoki mushrooms from China. This SIROI illustrates the importance of surveillance sampling, national and international coordination of efforts, and rapid information sharing to identify and stop foodborne outbreaks on a global scale. To reduce the risk of listeriosis illnesses linked to contaminated enoki mushrooms, public health and regulatory agencies in the United States and Canada remain committed to conducting comprehensive surveillance for Listeria in foods and in people, efficiently investigating identified outbreaks, and implementing control measures to potentially minimize the impact of future outbreaks.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".