Comparison of antimicrobial resistance patterns of Salmonella spp. and Escherichia coli recovered from pet dogs from volunteer households in Ontario (2005–06)
Bibliographic record
Abstract
OBJECTIVES: To compare the antimicrobial resistance (AMR) patterns of Salmonella spp. and Escherichia coli in the faeces of pet dogs from volunteer households in Southwestern Ontario, Canada. METHODS: From October 2005 to May 2006, 138 dogs from 84 Ontario households were recruited to participate in a cross-sectional study. Five consecutive daily faecal samples were collected from each dog and cultured for Salmonella spp. and E. coli. A panel of 15 antimicrobials from seven antimicrobial classes was used for susceptibility testing. RESULTS: E. coli and Salmonella spp. were recovered from 96.4% and 23.2% of dogs, respectively. In total, 515 bacterial isolates from 136 dogs from 83 households were sent for antimicrobial susceptibility testing with 80.4% of isolates being pan-susceptible. The most common resistance pattern was to amoxicillin/clavulanic acid, ampicillin, cefoxitin, ceftiofur and ceftriaxone, present in 13.3% of Salmonella isolates and 1.3% of E. coli isolates. Fifty-eight of the isolates were resistant to two or more drug classes, with 70.7% and 29.3% being E. coli and Salmonella, respectively. Based on multilevel logistic regression, the odds of resistance were greater in E. coli than Salmonella [odds ratio = 3.2; 95% confidence interval (CI) = 1.22-8.43]. Agreement in resistance between E. coli and Salmonella isolates from the same dog was low [prevalence-adjusted, bias-adjusted kappa (PABAK) = 0.38; 95% CI = 0.30-0.46]. CONCLUSIONS: Pet dogs are a potential household source of antimicrobial-resistant Salmonella spp. and E. coli. However, extrapolating the epidemiology of antimicrobial resistance in pathogens, like Salmonella, from E. coli should be done with caution.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 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".