Methicillin-resistant staphylococcal colonization in clinically normal dogs and horses in the community
Bibliographic record
Abstract
AIMS: To evaluate the prevalence of methicillin-resistant staphylococcal (MRS) colonization in clinically normal dogs and horses in the community. METHODS AND RESULTS: Three hundred clinically normal horses and 200 clinically normal dogs were enrolled. One nasal swab was collected from each horse. Two swabs were taken from each dog: (i) from an anterior nare, and (ii) a combination of the perineal area and 0.5 cm into the anus. Enrichment cultures were performed. Methicillin-resistant Staphylococcus aureus (MRSA) was not identified. Methicillin-resistant Staphylococcus intermedius (MRSI) was isolated from the nasal swab from three dogs. Methicillin-resistant coagulase negative staphylococci (MRCoNS) were isolated from 126/300 (42%) horses and 26/200 (13%) dogs. CONCLUSIONS: At present MRSI is not considered to be a significant zoonotic concern; however, it may become an important pathogen in dogs. MRCoNS mostly cause disease in compromised human or animal hosts. However, these bacteria can serve as reservoirs of resistance determinants in the community, which could lead to the emergence of novel MRSA strains. SIGNIFICANCE AND IMPACT OF THE STUDY: This is the first report of the prevalence of MRS colonization in clinically normal dogs in a community setting. Continued surveillance is indicated to determine whether MRSA will emerge in the animal population and become a concern for animal disease and zoonotic infection.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".