Prevalence of meticillin‐resistant <i>Staphylococcus pseudintermedius</i> (MRSP) from skin and carriage sites of dogs after treatment of their meticillin‐resistant or meticillin‐sensitive staphylococcal pyoderma
Bibliographic record
Abstract
BACKGROUND: Meticillin-resistant staphylococci are significant pathogens in veterinary dermatology, yet longitudinal studies of the impact of routine antimicrobial therapy on emergence or resolution of resistance are lacking. OBJECTIVES: To determine the prevalence of meticillin-resistant staphylococci on skin and carriage sites in dogs with bacterial pyoderma and evaluate the prevalence of meticillin-resistant Staphylococcus pseudintermedius (MRSP) colonization after successful treatment of pyoderma. ANIMALS: One hundred and seventy-three dogs that presented to a dermatology referral service with pyoderma and 41 healthy control dogs. METHODS: Skin, nasal and rectal swabs for bacterial culture were collected at the time of referral and after clinical resolution of the pyoderma. Meticillin resistance was confirmed by demonstration of penicillin binding protein 2a antigen. RESULTS: Initially, skin cultures yielded MRSP in 70 (40.5%) dogs, meticillin-resistant Staphylococcus aureus (MRSA) in three (1.7%) and meticillin-resistant Staphylococcus schleiferi ssp. coagulans (MRSScoag) in five (2.9%). Samples collected from the nose and rectum (carriage sites) yielded MRSP in 59 (34.1%) dogs, MRSA in 11 (6.4%) and MRSScoag in seven (4.0%). One hundred and two dogs were available for follow-up cultures after clinical cure. Of 42 dogs initially diagnosed with MRSP pyoderma, MRSP was isolated at follow-up from skin in 19 (45.2%) and carriage sites in 20 (47.6%). Of 60 dogs that did not have MRSP pyoderma initially, MRSP was isolated post-treatment from the skin in 17 (28.3%), and MRSP from carriage sites increased from 7.8% (initially) to 26.7% (P = 0.0022). CONCLUSIONS AND CLINICAL IMPORTANCE: Colonization by MRSP often persists after resolution of MRSP pyoderma. Acquisition of MRSP during treatment appears to be common.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".