Evaluation of Pet‐Related Management Factors and the Risk of <i>Salmonella</i> spp. Carriage in Pet Dogs from Volunteer Households in Ontario (2005–2006)
Bibliographic record
Abstract
The purpose of this study was to determine pet-related management factors that may be associated with the presence of Salmonella spp. in feces of pet dogs from volunteer households. From October 2005 until May 2006, 138 dogs from 84 households in Ontario were recruited to participate in a cross-sectional study. Five consecutive daily fecal samples were collected from each dog and enrichment culture for Salmonella spp. was performed. A higher than expected number of the dogs (23.2%; 32/138) had at least one fecal sample positive for Salmonella, and 25% (21/84) of the households had at least one dog shedding Salmonella. Twelve serotypes of Salmonella enterica subsp. enterica were identified, with the predominant serotypes being Typhimurium (33.3%; 13/39), Kentucky (15.4%; 6/39), Brandenburg (15.4%; 6/39) and Heidelberg (12.8%; 5/39). Univariable logistic regression models were created with a random effect for household to account for clustering. Statistically significant risk factors for a dog testing positive included having contact with livestock, receiving a probiotic in the previous 30 days, feeding a commercial or homemade raw food diet, feeding raw meat and eggs, feeding a homemade cooked diet, and having more than one dog in the household. In two-variable models that controlled for feeding raw food, the non-dietary variables were no longer statistically significant. These results highlight the potential public health risk of including raw animal products in canine diets.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".