Fermenting a place in history: The first outbreak of <i>Escherichia coli</i> O157 associated with kimchi in Canada
Bibliographic record
Abstract
(STEC) O157 was identified through whole genome sequencing (WGS). Exposure information was collected through case interviews. Traceback investigations were conducted, and samples from case homes, retail, and the manufacturer were tested for STEC O157. Fourteen cases were identified in two provinces in Western Canada, with isolates related by 0-5 whole genome multi-locus sequence typing allele differences. Symptom onset dates ranged from 11 December 2021 to 7 January 2022. The median age of cases was 29.5 (range 0-61); 64% were female. No hospitalisations or deaths were reported. Of 11 cases with information available on fermented vegetable exposures, 91% (10/11) reported consuming Kimchi Brand A during their exposure period. The traceback investigation identified Manufacturer A in Western Canada as the producer. One open and one closed sample of Kimchi Brand A tested positive for STEC O157, with isolates considered genetically related by WGS to the outbreak strain. Napa cabbage within the kimchi product was hypothesised as the most likely source of contamination. This paper summarises the investigation into this STEC O157 outbreak associated with kimchi, the first reported outside of East Asia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".