Application of a novel decontamination process using gaseous ozone
Bibliographic record
Abstract
Environmental disinfection in a health care setting is an important aspect of infection control. Recently, there has been interest in the use of vapor- and gas-based treatments for decontamination of surfaces and rooms. We describe preliminary results for an ozone-based decontamination of surfaces seeded with a range of vegetative cells and spores of bacteria of clinical relevance. The efficacy of the approach for room sanitization was also assessed. The protocol included use of a quenching agent to rapidly reduce ozone concentrations to safe levels allowing treatment times of less than 1 h for the majority of organisms tested. Using bacteria seeded onto agar plates and solid surfaces, reductions in bacterial load of greater than 3 log values were recorded for a number of organisms including Escherichia coli and methicillin-resistant Staphylococcus aureus. Application of the process in a 30 m3 room showed similar reductions in viable counts for these organisms and for Clostridium difficile spores. We suggest that the potential of this ozone-quench approach should be further evaluated for disinfection or decontamination of healthcare environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".