Identification of Methicillin‐Resistant<i>Staphylococcus aureus</i>Carriage in Less than 1 Hour during a Hospital Surveillance Program
Bibliographic record
Abstract
BACKGROUND: Methicillin-resistant Staphylococcus aureus (MRSA) has spread worldwide and is responsible for significant morbidity, mortality, and health care costs. Control strategies to limit the emergence and spread of this organism rely on rapid and sensitive tests for detection of MRSA carriage. However, the standard surveillance culture method for detecting MRSA is labor intensive and time-consuming (2-3 days per procedure). There is thus a need for a rapid and accurate method to screen for MRSA carriage. METHODS: We recently developed an easy-to-use real-time polymerase chain reaction (PCR) assay suitable for specific detection of MRSA in nasal specimens in <1 h. We studied the efficacy of our new PCR assay in routine screening for nasal MRSA carriage during a hospital surveillance program. A total of 331 nasal specimens obtained from 162 patients at risk for colonization were tested by both the standard mannitol agar culture method and our PCR assay. RESULTS: The PCR assay detected MRSA in all 81 samples that were culture positive for MRSA. The PCR assay detected 4 additional MRSA-positive specimens, for a specificity of 98.4%, a positive predictive value of 95.3%, and a sensitivity and negative predictive value of 100%. CONCLUSIONS: This novel PCR assay allows reliable identification of MRSA carriers in <1 h. This test should facilitate the efficacy of MRSA surveillance programs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".