Nationwide analysis of methicillin-resistant staphylococci in cats and dogs: resistance patterns and geographic distribution
Bibliographic record
Abstract
Objective: This study aims to quantify the frequency and resistance patterns of 3 methicillin-resistant staphylococci (MRS), Staphylococcus aureus (MRSA), Staphylococcus pseudintermedius (MRSP), and Staphylococcus schleiferi (MRSS), in companion animals, using historical culture and susceptibility data from a national diagnostic laboratory. Methods: Samples from cats and dogs across the US, between 2019 and 2022, were analyzed. Methicillin-resistant isolates identified according to Clinical and Laboratory Standards Institute VET01S (5th ed) were included. Data included location, patient species, sampling site, year, and susceptibility results for various panels of antimicrobials. Results: There were 110,423 MRSP, 5,618 MRSA, and 20,934 MRSS isolates identified. Methicillin-resistant S pseudintermedius was predominantly found in dogs (96.2%), with skin and soft tissue being the most common sites. Methicillin-resistant S aureus and MRSS were also primarily isolated from dogs, with significant yearly, regional, and species-specific differences in antimicrobial susceptibility observed. This study highlights high resistance levels in MRSP isolates, while MRSA and MRSS showed relatively higher susceptibility to several antimicrobials. Conclusions: This study provides insight into the distribution and antimicrobial resistance patterns of MRSA, MRSP, and MRSS in companion animals in the US. Resistance rates for enrofloxacin, marbofloxacin, and chloramphenicol may be higher than reported in this analysis due to recent changes in MIC breakpoints in the Clinical and Laboratory Standards Institute VET01S (7th ed). The findings underscore significant geographical and temporal variations in resistance, emphasizing the need for tailored antimicrobial stewardship programs. Clinical Relevance: The prevalence of MRS in companion animals poses treatment challenges and potential zoonotic risks. This study provides nationwide insight that was not previously available.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".