Associations Among Antimicrobial Use and Antimicrobial Resistance of <i>Salmonella</i> spp. Isolates from 60 Alberta Finishing Swine Farms
Bibliographic record
Abstract
The study objectives were to identify potential associations between reported antimicrobial use (AMU) practices and antimicrobial resistance (AMR) of fecal and environmental Salmonella spp. isolates (n = 322 isolates) recovered from 60 Alberta finishing swine farms, and to estimate the amount of pen and farm level variation in AMR. The AMU data were collected through a questionnaire. Separate multilevel logistic regression models were built for six antimicrobials with prevalence of resistance >or=5% using the Generalized Linear Latent and Mixed Model (GLLAMM) procedure. In-feed use of tylosin in finishers was associated with increased odds of resistance in Salmonella isolates to ampicillin (OR = 61.56), streptomycin (OR = 11.70), and multiple antimicrobials (OR = 4.90). Injectable penicillin use in growers was associated with decreased odds of resistance in Salmonella isolates to streptomycin (OR = 0.06), kanamycin (OR = 0.03), and multiple antimicrobials (OR = 0.12). Injectable penicillin use in finishers was associated with decreased odds of resistance in Salmonella isolates to ampicillin (OR = 0.007) and chloramphenicol (OR = 0.04). Overall, these results indicate that AMU in pig production is inconsistently associated with AMR in Salmonella from finishing swine. Variation in AMR prevalence of Salmonella isolates of swine was moderate to high at pen and farm levels for most antimicrobials suggesting that interventions at the pen and farm levels might be beneficial in reducing the emergence of AMR Salmonella in swine populations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".