An Overview of Listeriosis Outbreak Investigations in the United States Linked to Imported Enoki Mushrooms and Associated Regulatory Activities, Research, and Food Safety Knowledge Gaps
Bibliographic record
Abstract
Foods implicated in listeriosis outbreaks continue to change over time. Historically, listeriosis outbreaks have been primarily linked to consumption of deli meat and dairy products. More recently, they have been linked to vegetable row crops, fruits, and other produce, including imported specialty mushrooms. Specialty mushrooms, including enoki mushrooms, are popular in Japanese, Chinese, and Korean cuisines, and are commonly used in soups, stir-fries, hotpots, and salads. These mushrooms are imported into the U.S. from a variety of East Asian countries and Canada. Recently, food safety authorities around the world have linked listeriosis outbreaks to contaminated enoki mushrooms, leading to a series of highly impactful recalls. This review examines outbreaks and recalls associated with enoki mushrooms, related risks and food safety concerns, ongoing research, regulatory activities focused on this commodity, consumer preparation and handling practices, and prevention strategies. The exchange of epidemiologic and traceback evidence, genomic data, and international data sharing helped investigators find the source of multiple listeriosis outbreaks linked to enoki mushrooms. Since enoki mushrooms were first linked to listeriosis illnesses in 2020, state, federal, and international partners developed a strategy for outbreak prevention, including enhanced surveillance and an improved investigational approach for this commodity.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".