Outbreak of E. coli O157:H7 associated with lettuce served at fast food chains in the Maritimes and Ontario, Canada, Dec 2012
Bibliographic record
Abstract
BACKGROUND: Identification and control of multi-jurisdictional foodborne illness outbreaks can be complex because of their multidisciplinary nature and the number of investigative partners involved. OBJECTIVE: O157:H7 outbreak in Canada that highlights the importance of early notification and collaboration and the value of centralized interviewing. METHODS: Investigators from local, provincial and federal jurisdictions, using a national outbreak response protocol to clarify roles and responsibilities and facilitate collaboration, conducted a rapid investigation that included centralized re-interview of cases, descriptive methods, binomial probability, and traceback findings to identify the source of the outbreak. RESULTS: There were 31 laboratory confirmed cases identified in New Brunswick, Nova Scotia, and Ontario. Thirteen cases (42%) were hospitalized and one case (3%) developed hemolytic uremic syndrome; there were no deaths. Due to early notification a coordinated investigation was initiated before laboratory subtyping was available. Re-interview of cases identified 10 cases who had not initially reported exposure to the source of the outbreak. Less than one week after the Outbreak Investigation Coordinating Committee was formed, consumption of shredded lettuce from a fast food chain was identified as the likely source of the illnesses and the implicated importer/processor initiated a precautionary recall the same day. CONCLUSION: This outbreak investigation highlights the importance of early notification, prompt re-interviewing and collaboration to rapidly identify the source of an outbreak.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 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) | 0.001 | 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".