Prevalence and Risk Factors of <i>Staphylococcus aureus</i> Nasal Colonization in Horses Admitted to a Veterinary Teaching Hospital
Bibliographic record
Abstract
BACKGROUND: Methicillin-resistant Staphylococcus aureus (MRSA) is a major cause of nosocomial infections, including in veterinary settings. HYPOTHESIS/OBJECTIVES: To investigate the prevalence, risk factors for Staphylococcus aureus (SA) and MRSA colonization, and the duration of MRSA colonization. ANIMALS: Elective cases admitted to the Veterinary Teaching Hospital were recruited (228 horses). METHODS: A cross-sectional study was conducted over 3 years. Nasal swabs were collected at admission and cultured for SA. Methicillin-resistant isolates were identified using matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) technology, oxacillin minimal inhibitory concentration (MIC), and PCR testing. Horses colonized with MRSA were resampled until two negative cultures were obtained. Stabling management, activity, and medical history were obtained from owners and medical files. Multivariable logistic regressions were used to model associations between risk factors and colonization. RESULTS: The prevalence of SA and of MRSA nasal carriage was 17.5% (95% CI: 12.4-22.7) and 6.2% (95% CI: 2.9-9.4), respectively. Of the 10 horses colonized by MRSA and monitored over time, only one tested positive after 3 months. More than 10 horses on the premises (OR 6.0 - 95% CI 1.1-64.2), previous hospitalization (OR 6.0 - 95% CI 1.0-35.2), and year of admission (2022 vs. 2020-2021; OR 9.0 - 95% CI 1.7-92.2) were associated with MRSA nasal carriage. CONCLUSIONS AND CLINICAL IMPORTANCE: The prevalence of MRSA nasal colonization is of concern; however, the carriage seems transitory. Apart from the medical risk factors, the importance of social interactions in MRSA transmission needs to be elucidated in horses.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".