Antimicrobial resistance profiles of common mastitis pathogens on Canadian dairy farms
Bibliographic record
Abstract
Monitoring of antimicrobial resistance (AMR) in bacteria has clinical and public health significance. The present study determined prevalence of AMR in common mastitis pathogens Staphylococcus aureus, including methicillin-resistant Staph. aureus (MRSA; n=1,810), Escherichia coli (n=394), and Klebsiella species (n=139), including extended-spectrum β-lactamase (ESBL)-producing E. coli and Klebsiella species, isolated from milk samples on 89 dairy farms in 6 Canadian provinces. Minimum inhibitory concentrations (MIC) were determined using the Sensititer bovine mastitis plate (Trek Diagnostic Systems Inc., Cleveland, OH) and a National Antimicrobial Resistance Monitoring System gram-negative panel containing antimicrobials commonly used for mastitis treatment and control. Denim blue chromogenic agar and real-time PCR were used to screen and confirm MRSA, respectively. Resistance proportion estimates ranged from 0% for cephalothin and oxacillin to 8.8% for penicillin in Staph. aureus isolates, and 15% of the resistant Staph. aureus isolates were multidrug resistant. One MRSA isolate was confirmed (prevalence: 0.05%). Resistance proportion estimates ranged from 0% for ceftriaxone and ciprofloxacin to 14.8% for tetracycline in E. coli, and 0% for amikacin, ceftiofur, ciprofloxacin, and nalidixic acid to 18.6% for tetracycline in Klebsiella species isolates. Further, 62.8 and 55% of the resistant E. coli and Klebsiella species isolates were multidrug resistant, respectively. Resistance to >5 and >2 antimicrobials was most common in E. coli and Klebsiella species isolates, respectively, and no ESBL producers were found. Prevalence of AMR in bovine mastitis pathogens was low. Most gram-negative udder pathogens were multidrug resistant; MRSA was rarely found, and ESBL E. coli and Klebsiella species isolates were absent in Canadian milk samples.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 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".