Evaluation of the Risk Factors for Shedding <i>Salmonella</i> with or without Antimicrobial Resistance in Swine Using Multinomial Regression Method
Bibliographic record
Abstract
A multinomial logistic regression method was used to investigate the risk factors for antimicrobial resistance (AMR) in Salmonella isolated from faecal samples collected on 80 Ontario swine farms in Canada. The samples were classified into three groups including Salmonella-negative samples (S-), Salmonella-positive samples without AMR (S+AMR-) and Salmonella-positive samples with AMR (S+AMR+). The samples collected directly from pigs had a greater chance to be positive for Salmonella with AMR compared to those samples collected from the pen floor. The odds of culturing Salmonella with or without AMR was higher if pelleted feed was used compared with mash or liquid feed (P < 0.001). The faecal samples collected on farrow-to-finish farms had a significant lower chance of testing positive for Salmonella with multidrug resistance than the samples from grow-finisher farms (P = 0.004). The chance of culturing Salmonella without AMR on farms with a continuous system was higher than on farms with an all-in/all-out system (P = 0.009). However, there was no significant association between the flow system and recovery of Salmonella with AMR. The larger farms were more likely to be in S+AMR+ group than in S- group (P < 0.001) whereas herd size did not appear as a risk factor for being in S+AMR- group compared with S- group. These findings indicate that although on-farm antimicrobial use is one component of resistance, there might be other farm management factors that also affect the development of emerging resistant bacterial foodborne pathogens on swine farms. Finding different risk factors for shedding Salmonella with or without antimicrobial resistance would help to take the appropriate approach to each group if a control programme were to be implemented or an intervention applied.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".