IMPROVING CONTROL STRATEGIES OF INFECTIONS BY RESISTANT PATHOGENS IN A HOSPITAL NETWORK
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The efficacy of infection prevention and control on several hospital networks is assessed. We tested two kinds of strategy, a network-topology-based allocation and a [Formula: see text]-based allocation, where [Formula: see text] is the basic reproduction number of the infection. For this, a multi-patch deterministic model simulates the spread of carbapenemase-producing Enterobacteriaceae in several theoretical hospital networks parametrized by data from Brazil. Our results show that: (i) the allocation methods based on the [Formula: see text] of the hospitals may work better than the network-topology-based allocations; (ii) results from control efficacy for a specific hospital network cannot be generalized to other types of networks. Putting together the global network topology with local factors that drive pathogens transmission, the [Formula: see text]-based allocation method seems to be enough to control of healthcare-associated infections. Overall, the obtained results emphasize the importance of data collection on infection transmission and patient transfers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it