Effect of Patterns of Transferring Patients among Healthcare Institutions on Rates of Nosocomial Methicillin-Resistant<i>Staphylococcus aureus</i>Transmission: A Monte Carlo Simulation
Bibliographic record
Abstract
OBJECTIVE: To determine the effect of the rate and pattern of patient transfers among institutions within a single metropolitan area on the rates of methicillin-resistant Staphylococcus aureus (MRSA) transmission among patients in hospitals and nursing homes. METHODS: A stochastic, discrete-time, Monte Carlo simulation was used to model the rate and spread of MRSA transmission among patients in medical institutions within a single metropolitan area. Admission, discharges, transfers, and nosocomial transmission were simulated with respect to different interinstitutional transfer strategies and various situational scenarios, such as outlier institutions with high transmission rates. RESULTS: The simulation results indicated that transfer patterns and transfer rate changes do not affect nosocomial MRSA transmission. Outlier institutions with high transmission rates affect the system wide rate of nosocomial infections differently, depending on institution type. CONCLUSION: It is worth effort to understanding disease-transmission dynamics and interinstitutional transfer patterns for the management of recently introduced diseases or strains. Once endemic in a system, other strategies for transmission control need to be implemented.
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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 | 0.001 | 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 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".