A case–case study comparing the individual risk factors and symptomatology of <i>Salmonella</i> Heidelberg and <i>Salmonella</i> Typhimurium in Ontario in 2015, following implementation of the Ontario Investigation Tools
Bibliographic record
Abstract
Salmonella Heidelberg and Salmonella Typhimurium are among the most common serotypes responsible for human salmonellosis in Ontario. Introduction of the Ontario Investigation Tools (OIT) in 2014 allowed for standardized case investigation and reporting. This study compared the risk factors and symptomatology for sporadic S. Heidelberg and S. Typhimurium cases reported in Ontario in 2015, following implementation of the OIT. Multilevel logistic regression models were applied to assess associations between serotype and individual-level demographic characteristics, exposures and symptoms for sporadic confirmed cases of S. Heidelberg and S. Typhimurium in Ontario in 2015. There were 476 sporadic cases of S. Typhimurium (n = 278) and S. Heidelberg (n = 198) reported in Ontario in 2015. There were significant associations between the odds of the isolate from a case being one of these serotypes, and travel, consumption of sprouts (any type), contact with reptiles and development of malaise, fever or bloody diarrhoea. The S. Typhimurium and S. Heidelberg cases differed in both symptom presentation and risk factors for illness. Case-case comparisons of Salmonella serotypes have some advantages over case-control studies in that these are less susceptible to selection and recall bias while allowing for rapid comparison of cases to identify potential high-risk exposures that are unique to one of the serotypes when compared to the other. Comparing cases of two different Salmonella serotypes can help to highlight risk factors that may be uniquely associated with one serotype, or more strongly associated with one serotype compared to another. This information may be useful for understanding relative source attribution between common serotypes of Salmonella.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".