Prevalence of Antimicrobial Resistance and Characteristics of Escherichia coli Isolates From Fecal and Manure Pit Samples on Dairy Farms in the Province of Québec, Canada
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is an important burden for public health and veterinary medicine. For Québec (Canada) dairy farms, the prevalence of AMR is mostly described using passive surveillance, which may be misleading. In addition, the presence of extended spectrum β-lactamase (ESBL)/AmpC producing Escherichia coli is unknown. This observational cross-sectional study used random dairy farms (n = 101) to investigate AMR and extended spectrum β-lactamase (ESBL)/AmpC producing Escherichia coli. Twenty antimicrobials were tested on E. coli isolates (n = 593) recovered from fecal samples (n = 599) from calves, cows, and the manure pit. Isolates were mostly susceptible (3% AMR or less) to the highest priority critically important antimicrobials in humans. The highest levels of AMR were to tetracycline (26%), sulfisozaxole (23%) and streptomycin (19%). The resistance genes responsible for these resistances were, respectively: tet(A), tet(B), sul1, sul2, sul3, aph(3”)-Ib (strA), aph(6)-Id (strB), aadA1, aadA2, and aadA5. ESBL analysis revealed two predominant phenotypes: AmpC (51%) and ESBL (46%) where blaCMY−2 and blaCTX−M(blaCTX−M−1, blaCTX−M−15, and blaCTX−M−55) were the genes responsible for these phenotypes, respectively. During this study, 85% of farms had at least one ESBL/AmpC producing E. coli. Isolates from calves were more frequently resistant than those from cows or manure pits. Although prevalence of AMR was low for critically important antimicrobials, there was a high prevalence of ESBL/AmpC-producing E. coli on Quebec dairy farms, particularly in calves. Those data will help determine a baseline for AMR to evaluate impact of initiatives aimed at reducing AMR.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".