Use of fluorescent tagging for assessment of environmental cleaning and disinfection in a veterinary hospital
Bibliographic record
Abstract
Environmental cleaning was assessed at a small animal veterinary referral hospital and associated primary healthcare facility. A convenience sample of surfaces was contaminated with fluorescent dye, and then cleaning was assessed 24 hours later by UV light visualisation. Five hundred sixty-three sites were assessed; however, 70 sites were unable to be evaluated 24 hours later because equipment had been removed or because rooms were occupied at the time of re-evaluation. Overall, dye was removed from 212/493 (43%) of sites. Site-specific rates ranged from 14% (computer keyboards and mice, 9/66 site cleaned) to 81% (examination tables, 44/54 sites cleaned). There was a significant difference in the prevalence of successful cleaning by general location (P < 0.0001) and surface type (P < 0.0001). Environmental tagging was an easy and low-cost tool to assess cleaning practices. Results prompted further infection control investigations to explain selected deficiencies, leading to identification of inadequacies in protocols and practices. Environmental tagging may be a useful infection control tool for establishing baseline cleaning rates, identifying deficiencies in protocols, evaluating the effects of interventions and education of personnel.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".