Antimicrobial susceptibility of mastitis pathogens isolated from North American dairy cattle, 2011-2022
Bibliographic record
Abstract
A total of 10,890 bacterial isolates of Streptococcus dysgalactiae, Streptococcus uberis, Staphylococcus aureus and Escherichia coli isolated as etiological agents from dairy cows with mastitis by 29 veterinary laboratories across North America between 2011 and 2022 were tested for in vitro antimicrobial susceptibility by broth microdilution to ampicillin, cefoperazone, ceftiofur, cephalothin, erythromycin, oxacillin, penicillin-novobiocin and pirlimycin according to CLSI standards. Using available clinical breakpoints, antimicrobial resistance among S. dysgalactiae (n = 2406) was low for penicillin-novobiocin (0% resistance), ceftiofur (0.1%), erythromycin (3.2%) and pirlimycin (4.6%). Among S. uberis (n = 2398), resistance was low for ampicillin (0%) and ceftiofur (0.2%) and moderate for erythromycin (11.9%) and pirlimycin (18.4%). For S. aureus (n = 3194), resistance was low for penicillin-novobiocin (0%), ceftiofur (0.1%), oxacillin (0.2%), erythromycin (0.7%), cefoperazone (1.2%) and pirlimycin (2.8%). For E. coli (n = 2892), resistance was low for ceftiofur (2.8%) and cefoperazone (3.4%) and moderate for ampicillin (9.2%). Overall, the results indicate that mastitis pathogens in the United States and Canada have not shown any substantial changes in the in vitro susceptibility to antimicrobial drugs over the 12 years of the study, or among that of the proceeding survey from 2002-2010. The data support the conclusion that resistance to common antimicrobial drugs among mastitis pathogens, even to drugs that have been used in dairies for mastitis management for many years, continues to remain low.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".