Comparing and optimizing ultraviolet germicidal irradiation systems use for patient room terminal disinfection: an exploratory study using radiometry and commercial test cards
Bibliographic record
Abstract
Abstract Background Ultraviolet germicidal irradiation (UVGI) systems are gaining popularity, however objective comparisons of their characteristics are lacking. While environmental cultures and reduction of hospital-associated infections rates are excellent study endpoints, they are impractical for centers with limited resources who want to compare or optimize UVGI systems use. Methods We evaluated radiometry and commercial test cards, two simple and low cost tools, to compare 2 full size UVGI systems (Tru-D and Optimum-UV Enlight) and 2 small units (Lumalier EDU 435 and MRSA-UV Turbo-UV). Results Radiometry-derived output curves show that if both large devices emit enough energy to reach C. difficile lethal doses at 10 ft, the reduction in output in distance is almost perfectly logarithmic. In a patient room environment, Enlight and Tru-D performed similarly when compared using radiometry and commercial test cards. The two small devices reached C. difficile range around the bathroom with the device raised above the floor, but longer times are needed. Conclusions Despite different workflows and price points, no clear superiority emerges between Tru-D and Enlight. Bathroom disinfection should be dealt with separately from the main room and small, cheaper units can be used. Radiometry and commercial test cards are promising ways to compare UVGI systems, but further validation is needed using correlation with environmental cultures. Trial registration Not applicable.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".