Transmission of MRSA, ESBL <i>E. coli</i>, and <i>C. difficile</i> within a tertiary care hospital and across surrounding facilities in Japan: a molecular epidemiological study with the PCR-based Open-reading frame typing
Bibliographic record
Abstract
Abstract Objective: To determine the regional impact of transmission of multidrug-resistant organisms (MRDOs) and Clostridioides difficile ( C. difficile ) among a tertiary care hospital and surrounding facilities including long-term care facilities (LTCFs). Design: Retrospective cohort study. Methods: Patients admitted to a tertiary care hospital from July 2019 to July 2021 were recruited if their clinically collected cultures grew the following pathogens: Methicillin-resistant Staphylococcus aureus (MRSA), Extended-Spectrum Beta-Lactamase (ESBL) producing Enterobacterales, Pseudomonas aeruginosa with difficult-to-treat resistance, Carbapenem-resistant Enterobaterales, Vancomycin-resistant Enterococci, and C. difficile . Patient characteristics including admission and discharge pathway were collected. For the isolates of MRSA, ESBL-producing Escherichia coli ( E. coli ), and C. difficile , a molecular epidemiological analysis was conducted, utilizing the PCR-based Open-Reading Frame Typing (POT) method. Results: Three hundred-five patients were identified with a total of 332 culture specimens of the target pathogens. The top three were 132 MRSA isolates (43.3%, out of 305), 97 ESBL E. coli (31.8%), and 32 ESBL Enterobacterales (non- E. coli ) (10.5%). The target pathogens were more detectable within 3 days among patients admitted from LTCFs or other hospitals than those admitted from home (Odds Ratio 4.6, 95% confidence interval 2.8-7.6, p-value < 0.001). The molecular epidemiological analysis suggested the transmissions of MRSA, ESBL E. coli and C. difficile occurred 52 out of 111 patients within the in-hospital environment, and 7 out of 128 within the prehospital environment, respectively. Conclusions: MDROs/ C. difficile transmission is prevalent within a tertiary care hospital and further complicated by its inter-facility transmission across surrounding LTCFs and hospitals in Japan.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".