IMPROVING CONTROL STRATEGIES OF INFECTIONS BY RESISTANT PATHOGENS IN A HOSPITAL NETWORK
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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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".