Methicillin‐Resistant Staphylococcal Contamination of Clothing Worn by Personnel in a Veterinary Teaching Hospital
Bibliographic record
Abstract
OBJECTIVE: To determine the methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-resistant Staphylococcus pseudintermedius (MRSP) contamination rate of white coats (WC) and surgical scrubs (SS) worn by personnel at the Ontario Veterinary College Health Sciences Centre (OVCHSC) and to identify risk factors associated with clothing contamination. STUDY DESIGN: Cross-sectional study. SAMPLE POPULATION: Personnel including clinical faculty, house officers, technicians, and veterinary students working at the OVCHSC. METHODS: Electrostatic cloths were used to sample WC and SS of hospital personnel. Samples were tested for MRSA and MRSP and isolates were typed. Participants completed a self-administered questionnaire and data was evaluated for risk factors. RESULTS: Of 114 specimens, MRS were isolated from 20 (17.5%), MRSA from 4 (3.5%), and MRSP from 16 (14.0%). Technicians were 9.5× (OR = 0.95, 95% CI: 1.2-∞, P = .03) more likely than students to have clothing contaminated with MRSA. No risk factors were identified for MRSP or for overall MRS contamination. CONCLUSIONS: Standard hospital clothing was found to have a high prevalence of MRS contamination in a veterinary teaching hospital and could be a source of hospital-acquired infections.
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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 | 0.001 |
| Research integrity | 0.000 | 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".