Everything but the kitchen sink: The use of multiple hypothesis generation methods to investigate an outbreak of <i>Salmonella</i> Enteritidis associated with frozen profiteroles and eclairs
Bibliographic record
Abstract
Abstract In December 2018, an outbreak of Salmonella Enteritidis infections was identified in Canada by whole-genome sequencing (WGS). An investigation was initiated to identify the source of the illnesses, which proved challenging and complex. Microbiological hypothesis generation methods included comparisons of Salmonella isolate sequence data to historical domestic outbreaks and international repositories. Epidemiological hypothesis generation methods included routine case interviews, open-ended centralized re-interviewing, thematic analysis of open-ended interview data, collection of purchase records, a grocery store site visit, analytic comparison to healthy control groups, and case–case analyses. Food safety hypothesis testing methods included food sample collection and analysis, and traceback investigations. Overall, 83 cases were identified across seven provinces, with onset dates from 6 November 2018 to 7 May 2019. Case ages ranged from 1 to 88 years; 60% (50/83) were female; 39% (22/56) were hospitalized; and three deaths were reported. Brand X profiteroles and eclairs imported from Thailand were identified as the source of the outbreak, and eggs from an unregistered facility were hypothesized as the likely cause of contamination. This study aims to describe the outbreak investigation and highlight the multiple hypothesis generation methods that were employed to identify the source.
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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 | 0.110 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".