A Multidisciplinary Approach to Reducing Outbreaks and Nosocomial MRSA in a University-Affiliated Hospital
Bibliographic record
Abstract
Endemic MRSA (methicillin-resistant Staphylococcus aureus) colonization and infection has been shown to increase morbidity, length of stay and hospital cost. Prevention of transmission demands innovative approaches. Descriptive statistics were used to determine high-incidence units. On admission, patients with a history of previous admission to a healthcare institution within the past six months were screened for MRSA. Point prevalence studies were carried out on units with more than two nosocomial (hospital-acquired) MRSA patient isolates within a four-week period. A multidisciplinary team from Infection Control and clinical units determined potential contributing factors. Recommendations included increased organism-specific education for staff, environmental cleaning and elimination of sources of transmission. Control charts to monitor nosocomial incidence rates were provided to those units that historically had a high prevalence of MRSA infections and colonization. Compliance with the infection control isolation guidelines and screening guidelines was monitored by the service. There was a 60% decrease in nosocomial MRSA between 2000 and 2001. Unit feedback was extended throughout the hospital. This decrease has been sustained since 2001 with annual rates per 1000 patient-days of 0.61 for 2000, 0.21 for 2001, 0.24 for 2002, 0.25 for 2003, 0.35 for 2004 and 0.19 for 2005.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".