Risk Factors for Antimicrobial Resistance among Fecal <i>Escherichia coli</i> from Residents on Forty-Three Swine Farms
Bibliographic record
Abstract
Fecal Escherichia coli (n = 555) were isolated from 115 residents on 43 farrow-to-finish swine farms to determine the prevalence of antimicrobial resistance and associated risk factors. Susceptibility to 21 antimicrobials was determined and the overall prevalence of antimicrobial resistance was 25.8%. Pair-wise difference in prevalences of resistance to individual antimicrobials was significant between isolates from residents on farms that fed medicated swine rations compared to those that did not (p = 0.013). Cross-resistance among antimicrobials of same class and multidrug-resistance were observed. Logistic regression models revealed the following risk factors positively associated with antimicrobial resistance: use of antimicrobials in pigs on farms; number of hours per week that farmers spent in their pig barns; handling of sick pigs; and intake of antimicrobials by farm residents. This study indicates that occupational exposure of farmers to resistant bacteria and use of antimicrobials in pig farming may constitute a source of resistance in humans, although the human health impacts of such resistance is unknown. The consumption of antimicrobials by farmers appeared to constitute a significant risk for resistance development. Fecal E. coli from farm residents may act as a reservoir of resistance genes for animal and/or human pathogens.
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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 | 0.000 | 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".