Assessing the effect of patient screening and isolation on curtailing Clostridium difficile infection in hospital settings
Bibliographic record
Abstract
BACKGROUND: Patient screening at the time of hospital admission is not recommended as a routine practice, but may be an important strategy for containment of Clostridium difficile infection (CDI) in hospital settings. We sought to investigate the effect of patient screening in the presence of asymptomatic carriers and in the context of imperfect patient isolation. METHODS: We developed and parameterized a stochastic simulation model for the transmission dynamics of CDI in a hospital ward. RESULTS: We found that the transmission of CDI in the hospital, either through asymptomatic carriers or as a results of ineffective implementation of infection control practices, at the time of hospital admission. The results show that, for a sufficiently high reproduction number of CDI, the disease can persist within a hospital setting in the presence of in-ward transmission, even when there are no asymptomatically colonized patients at the time of hospital admission. CONCLUSIONS: Our findings have significant public health and clinical implications, especially in light of the emergence and community spread of hypervirulent CDI strains with enhanced transmission rates and toxin production. Rapid detection of colonized patients remains an important component of CDI control, especially in the context of asymptomatic transmission. Screening of in-hospital patients with potential exposure to colonized patients or contaminated environment and equipment can help reduce the rates of silent transmission of CDI through asymptomatic carriers.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".