Risks and Policies Focused on Hospital Infections and Contaminated Textiles
Bibliographic record
Abstract
Background: Recent published studies demonstrate that clinical textiles and other wearables are a source of hospital infections. This Workshop will address three aspects of the issue: a) a review of the current infectious outbreaks and their specific pathogens; b) the multiple publics at risk for healthcare infections; and c) the basis for current standards and future policies in the EU, US, and elsewhere. Workshop format: Introduction by DF Goldsmith; outbreak of pediatric mucormycosis in New Orleans by CDC scientists; methicillin-suspectible Staphylcocus aureus contamination of clinical staff lanyards in London; laundry contamination by zygomycetes in Hong Kong by KY Yuen; control of hospital infections by the use of copper oxide in Tel Aviv; Summary by MP Muller. The populations at risk for textile transmitted infections include patients, healthcare providers, the general public visiting hospitals and nursing homes; and laundry employees by DF Goldsmith.Standards and Policy Introduction by D Hoefer; Hohenstein Institute testing for <20 colony forming units (CFU) by D Hoefer; "Hygienically Clean" in the US by DF Goldsmith; improved technologies including improvements in detergents, bleaches and temperature; biocidical naocopper fiber treatments; and possible reduction to <15 CFU. Conclusions and Future Public Health Issues, Surveillance by DF Goldsmith and D Hoefer. Synthesis: Transmission of healthcare infections is an area needing more epidemiology research and leadership. This will become a higher concern with the findings of drug resistant pathogens and the need for improving prevention control technologies.
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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 | 0.031 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.021 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".