Universal vs Risk Factor Screening for Methicillin-Resistant<i>Staphylococcus aureus</i>in a Large Multicenter Tertiary Care Facility in Canada
Bibliographic record
Abstract
OBJECTIVE To assess the clinical effectiveness of a universal screening program compared with a risk factor-based program in reducing the rates of nosocomial methicillin-resistant Staphylococcus aureus (MRSA) among admitted patients at the Ottawa Hospital. DESIGN Quasi-experimental study. SETTING Ottawa Hospital, a multicenter tertiary care facility with 3 main campuses, approximately 47,000 admissions per year, and 1,200 beds. METHODS From January 1, 2006 through December 31, 2007 (24 months), admitted patients underwent risk factor-based MRSA screening. From January 1, 2008 through August 31, 2009 (20 months), all patients admitted underwent universal MRSA screening. To measure the effectiveness of this intervention, segmented regression modeling was used to examine monthly nosocomial MRSA incidence rates per 100,000 patient-days before and during the intervention period. To assess secular trends, nosocomial Clostridium difficile infection, mupirocin prescriptions, and regional MRSA rates were investigated as controls. RESULTS The nosocomial MRSA incidence rate was 46.79 cases per 100,000 patient-days, with no significant differences before and after intervention. The MRSA detection rate per 1,000 admissions increased from 9.8 during risk factor-based screening to 26.2 during universal screening. A total of 644 new nosocomial MRSA cases were observed in 1,448,488 patient-days, 323 during risk factor-based screening and 321 during universal screening. Secular trends in C. difficile infection rates and mupirocin prescriptions remained stable after the intervention whereas population-level MRSA rates decreased. CONCLUSION At Ottawa Hospital, the introduction of universal MRSA admission screening did not significantly affect the rates of nosocomial MRSA compared with risk factor-based screening. Infect. Control Hosp. Epidemiol. 2015;37(1):41-48.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".