Strategies to Prevent Transmission of Methicillin-Resistant<i>Staphylococcus aureus</i>in Acute Care Hospitals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Previously published guidelines are available that provide comprehensive recommendations for detecting and preventing healthcare-associated infections (HAIs). Our intent in this document is to highlight practical recommendations in a concise format to assist acute care hospitals in their efforts to prevent transmission of methicillin-resistant Staphylococcus aureus (MRSA). Refer to the Society for Healthcare Epidemiology of America/Infectious Diseases Society of America “Compendium of Strategies to Prevent Healthcare-Associated Infections” Executive Summary, Introduction, and accompanying editorial for additional discussion. 1. Burden of HAIs caused by MRSA in acute care facilities a. In the United States, the proportion of hospital-associated S. aureus infections that are caused by strains resistant to methicillin has steadily increased. In 2004, MRSA accounted for 63% of S. aureus infections in hospitals. b. Although the proportion of S. aureus –associated HAIs among intensive care unit (ICU) patients that are due to methicillin-resistant strains has increased (a relative measure of the MRSA problem), recent data suggest that the incidence of central line–associated bloodstream infection caused by MRSA (an absolute measure of the problem) has decreased in several types of ICUs since 2001. Although these findings suggest that there has been some success in preventing nosocomial MRSA transmission and infection, many patient groups continue to be at risk for such transmission. c. MRSA has also been documented in other areas of the hospital and in other types of healthcare facilities, including those that provide long-term care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it