Prevalence and patterns of antimicrobial resistance of fecal Escherichia coli among pigs on 47 farrow-to-finish farms with different in-feed medication policies in Ontario and British Columbia
Bibliographic record
Abstract
The main objectives of this study were to determine the prevalence and patterns of antimicrobial resistance in pigs on farms that medicated swine ration and those that did not. A total of 940 isolates of Escherichia coli from 188 pooled fecal samples obtained from weaner and finisher pigs on 47 farrow-to-finish swine farms (34 farms used in-feed medication and 13 did not) were tested for susceptibility to 21 antimicrobials using a breakpoint concentration method. The prevalence of resistance varied widely (0.0% to 81.3%) among the antimicrobials tested. Ninety percent of all the isolates tested were resistant to one or more antimicrobials. The most common multi-drug resistance patterns were to 2 to 6 antimicrobials. Resistance was significantly more frequent (P < 0.01) on farms that used in-feed medication compared to those that did not, and significantly more frequent (P < 0.01) in weaner pigs compared to finisher pigs. These findings indicate that resistance to a broad range of antimicrobials was prevalent among fecal E. coli isolates of pigs on study farms, and that this constitutes a potential reservoir for resistance genes that could spread to pathogens. The findings also provide further evidence that use of medication in swine rations provides selective pressure for antimicrobial resistance in E. coli in pigs.
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 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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".