Kanser Hastalarına Uygulanan Klorheksidin Glukonat Banyosunun MRSA ve VRE Kolonizasyonu Üzerindeki Etkileri: Çapraz Tasarımlı Bir Çalışma
Bibliographic record
Abstract
Evidence shows that bathing with chlorhexidine gluconate (CHG) solution reduces the colonization of microorganisms that cause healthcare-associated infections (HAIs). The aim of this study was to evaluate the effects of CHG bath on MRSA and VRE colonization in cancer patients hospitalized in the intensive care unit (ICU). This crossover design study compared standard soap + water baths and 2% CHG baths in cancer patients. Between September 2018 and July 2019, 78 patients were divided into two arms. Patients in the first arm were washed with soap + water for the first three days, followed by 2% CHG for three days. Interventions were administered to patients in the second arm in reverse order. During the control and intervention periods, a washout day was left between bath applications. Swab samples were taken from the nasal, groin and rectal areas before and after bathing. Samples inoculated on sheep blood medium were examined after 16-18 hours of incubation. Gram-positive isolates with positive catalase and coagulase tests were identified as Staphylococcus aureus. Methicillin resistance was determined by disk diffusion test using cefoxitin discs on Mueller-Hinton agar and confirmed by real-time PCR (Rt-PCR) BD MAX MRSA XT test (BD Diagnostics, BD-MAX system, Canada) showing the presence of mecA gene. Samples inoculated on chromID® VRE selective medium were examined after 24 hours of incubation. The suspicious colonies were identified as Enterococcus faecium by API-ID Strep. Vancomycin resistance was confirmed by Rt-PCR VIASURE test (BD Diagnostics, BD-MAX system, Amsterdam) for the presence of vanA and vanB genes. MRSA colonization was detected in six patients and VRE colonization was detected in nine patients. After starting the CHG bath, nasal MRSA colonization decreased in the first arm. When the arms were compared, MRSA colonization in nasal samples and VRE colonization in rectal samples were found to be higher in the first arm than in the second arm. Bathing practices in the ICUs are essential in preventing HAIs, which is one of the patient safety problems. The results of this study show that daily bathing with 2% CHG reduces nasal MRSA and rectal VRE colonization in cancer patients in the ICU.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".