Methicillin-resistant staphylococcal contamination of cellular phones of personnel in a veterinary teaching hospital
Bibliographic record
Abstract
BACKGROUND: Hospital-associated infections are an increasing cause of morbidity and mortality in veterinary patients. With the emergence of multi-drug resistant bacteria, these infections can be particularly difficult to eradicate. Sources of hospital-associated infections can include the patients own flora, medical staff and inanimate hospital objects. Cellular phones are becoming an invaluable feature of communication within hospitals, and since they are frequently handled by healthcare personnel, there may be a potential for contamination with various pathogens. The objective of this study was to determine the prevalence of contamination of cellular phones (hospital issued and personal) carried by personnel at the Ontario Veterinary College Health Sciences Centre with methicillin-resistant Staphylococcus pseudintermedius (MRSP) and methicillin-resistant Staphylococcus aureus (MRSA). RESULTS: MRSP was isolated from 1.6% (2/123) and MRSA was isolated from 0.8% (1/123) of cellular phones. Only 21.9% (27/123) of participants in the study indicated that they routinely cleaned their cellular phone. CONCLUSIONS: Cellular phones in a veterinary teaching hospital can harbour MRSP and MRSA, two opportunistic pathogens of significant concern. While the contamination rate was low, cellular phones could represent a potential source for infection of patients as well as infection of veterinary personnel and other people that might have contact with them. Regardless of the low incidence of contamination of cellular phones found in this study, a disinfection protocol for hospital-issued and personal cellular phones used in veterinary teaching hospitals should be in place to reduce the potential of cross-contamination.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".