Modeling Transmission of Methicillin-Resistant<i>Staphylococcus Aureus</i>Among Patients Admitted to a Hospital
Bibliographic record
Abstract
OBJECTIVE: To determine the impact of the screening test, nursing workload, handwashing rates, and dependence of handwashing on risk level of patient visit on methicillin-resistant Staphylococcus aureus (MRSA) transmission among hospitalized patients. SETTING: General medical ward. METHODS: Monte Carlo simulation was used to model MRSA transmission (median rate per 1,000 patient-days). Visits by healthcare workers (HCWs) to patients were simulated, and MRSA was assumed to be transmitted among patients via HCWs. RESULTS: The transmission rate was reduced from 0.89 to 0.56 by the combination of increasing the sensitivity of the screening test from 80% to 99% and being able to report results in 1 day instead of 4 days. Reducing the patient-to-nurse ratio from 4.3 in the day and 6.8 at night to 3.8 and 5.7, respectively, reduced the number of nosocomial infections from 0.89 to 0.85; reducing the ratio to 1 and 1, respectively, further reduced the number of nosocomial infections to 0.32. Increases in handwashing rates by 0%, 10%, and 20% for high-risk visits yielded reductions in nosocomial infections similar to those yielded by increases in handwashing rates for all visits (0.89, 0.36, and 0.24, respectively). Screening all patients for MRSA at admission reduced the transmission rate to 0.81 per 1,000 patient-days from 1.37 if no patients were screened. CONCLUSION: Within the ranges of parameters studied, the most effective strategies for reducing the rate of MRSA transmission were increasing the handwashing rates for visits involving contact with skin or bodily fluid and screening patients for MRSA at admission.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | high |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | high |
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".