Methicillin-Resistant<i>Staphylococcus aureus</i>Colonization in Personnel Attending a Veterinary Surgery Conference
Bibliographic record
Abstract
OBJECTIVE: To evaluate the prevalence of, and risk factors for, methicillin-resistant Staphylococcus aureus (MRSA) colonization in veterinary personnel. STUDY DESIGN: Cross-sectional study. SAMPLE POPULATION: Conference attendees (n=341) at the 2008 American College of Veterinary Surgeons Symposium in San Diego California. METHODS: Nasal swabs were collected and tested using selective culture for MRSA. Isolates were typed and risk factors were evaluated using questionnaire data. RESULTS: 17.3% of subjects (17% veterinarians and 18% technicians) were MRSA positive. Colonized individuals originated from 5 different countries, predominantly the United States and Canada. Contact with small ruminants in the preceding 30 days (odds ratio [OR] 2.2), living with a person diagnosed with MRSA in the preceding year (OR 19.8) and working in a clinic where there is a specific person in charge of the infection control program (OR 2.2) were associated with colonization using multivariable analysis. CONCLUSION: The high rate of colonization identified here provides more evidence that MRSA exposure is likely an occupational risk for veterinary personnel. The equal rates in small animal and large animal personnel were surprising and contradict earlier studies indicating greater rates among equine personnel. The association of MRSA and small ruminant contact has not been reported previously. CLINICAL RELEVANCE: MRSA is an important emerging pathogen in veterinary medicine and is a concern for both patients and veterinary personnel. The high colonization rate indicates the need to understand and control the spread of MRSA in veterinary clinics.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".