A model for choosing an automated ultraviolet-C disinfection system and building a case for the C-suite: Two case reports
Bibliographic record
Abstract
Environmental disinfection has become the new frontier in the ongoing battle to reduce the risk of health care-associated infections. Evidence demonstrating the persistent contamination of environmental surfaces despite traditional cleaning and disinfection methods has led to the widespread acceptance that there is both a need for reassessing traditional cleaning protocols and for using secondary disinfection technologies. Ultraviolet-C (UV-C) disinfection is one type of no-touch technology shown to be a successful adjunct to manual cleaning in reducing environmental bioburden. The dilemma for the infection preventionist, however, is how to choose the system best suited for their facility among the many UV-C surface disinfection delivery systems available and how to build a case for acquisition to present to the hospital administration/C-suite. This article proposes an approach to these dilemmas based in part on the experience of 2 health care networks.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.024 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".