Optimizing Contact Precautions to Curb the Spread of Antibiotic-resistant Bacteria in Hospitals: A Multicenter Cohort Study to Identify Patient Characteristics and Healthcare Personnel Interactions Associated With Transmission of Methicillin-resistant Staphylococcus aureus
Bibliographic record
Abstract
BACKGROUND: Healthcare personnel (HCP) acquire antibiotic-resistant bacteria on their gloves and gowns when caring for intensive care unit (ICU) patients. Yet, contact precautions for patients with methicillin-resistant Staphylococcus aureus (MRSA) remains controversial despite existing guidelines. We sought to understand which patients are more likely to transfer MRSA to HCP and to identify which HCP interactions are more likely to lead to glove or gown contamination. METHODS: This was a prospective, multicenter cohort study of cultured HCP gloves and gowns for MRSA. Samples were obtained from patients' anterior nares, perianal area, and skin of the chest and arm to assess bacterial burden. RESULTS: Among 402 MRSA-colonized patients with 3982 interactions, we found that HCP gloves and gowns were contaminated with MRSA 14.3% and 5.9% of the time, respectively. Contamination of either gloves or gowns occurred in 16.2% of interactions. Contamination was highest among occupational/physical therapists (odds ratio [OR], 6.96; 95% confidence interval [CI], 3.51, 13.79), respiratory therapists (OR, 5.34; 95% CI, 3.04, 9.39), and when any HCP touched the patient (OR, 2.59; 95% CI, 1.04, 6.51). Touching the endotracheal tube (OR, 1.75; 95% CI, 1.38, 2.19), bedding (OR, 1.43; 95% CI, 1.20, 1.70), and bathing (OR, 1.32; 95% CI, 1.01, 1.75) increased the odds of contamination. We found an association between increasing bacterial burden on the patient and HCP glove or gown contamination. CONCLUSIONS: Gloves and gowns are frequently contaminated with MRSA in the ICU. Hospitals may consider using fewer precautions for low-risk interactions and more for high-risk interactions and personnel.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".