Associations Between Feed and Water Antimicrobial Use in Farrow-to-Finish Swine Herds and Antimicrobial Resistance of Fecal <i>Escherichia coli</i> from Grow-Finish Pigs
Bibliographic record
Abstract
Escherichia coli (n = 1439), isolated from the feces of apparently healthy grow-finish pigs in 20 herds, were tested for susceptibility to 16 antimicrobials. Logistic regression models were developed for each resistance that was observed in more than 5% of the isolates. Each production phase's (suckling, nursery, grow-finish pigs or sows) antimicrobial exposure rate, through feed or water, was considered as a risk factor. Management variables were evaluated as potential confounders. Six resistance outcomes were associated with an antimicrobial use risk factor and four included exposures of pigs outside the grow-finish phase. In the case of sulfamethoxazole, the odds of resistance increased 2.3 times for every 100,000 pig-days of nursery pig exposure to sulfonamides. Thus, swine producers and veterinarians must be aware that antimicrobial use in pigs distant from market could have food safety repercussions. Five resistance outcomes were associated with exposure to an unrelated antimicrobial class. Most notably, the odds of sulfamethoxazole and chloramphenicol resistance were each six times higher in herds reporting high (more than 500/1,000 pig-days) grow-finish pig, macrolide exposure compared to herds with no macrolide use in grow-finish pigs. Therefore, the potential for co-selection should be considered in antimicrobial use decisions. This study emphasizes the importance of judicious antimicrobial use in pork production.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".