Self‐disinfecting sink drains reduce the <i>Pseudomonas aeruginosa</i> bioburden in a neonatal intensive care unit
Bibliographic record
Abstract
AIM: Water in sink drains is a known source of gram-negative bacteria. We aimed to evaluate the impact of self-disinfecting sink drains on the emission of aerosolised bacteria and on Pseudomonas aeruginosa acquisition among neonates. METHODS: Aerosol bacterial growth and patient Pseudomonas aeruginosa acquisition rates were measured at baseline (Phase One), for 13 months after sinks were relocated or redesigned during refurbishment (Phase Two) and for 13 months after introducing self-disinfecting sink drains (Phase Three). RESULTS: Cultures were positive for bacterial growth in 56%, 24% and 13% of the tested aerosols in Phases One, Two and Three, respectively. Comparing Phases Two and Three produced an odds ratio (OR) of 0.47, with a 95% confidence interval (CI) of 0.22-0.99 (p = 0.047), for all bacteria and an OR of 0.31 and CI of 0.12-0.79 (p = 0.013) for Pseudomonas aeruginosa. Rates of Pseudomonas aeruginosa positive clinical cultures were 0.34, 0.27 and 0.13 per 1000 patient days during the respective phases, with a significant increase of time to the next positive clinical culture in Phase Three. CONCLUSION: Self-disinfecting sink drains were superior to sink replacements in preventing emissions from aerosols pathogens and may reduce hospital-acquired infections. The bioburden reduction should be confirmed in a larger multicentre trial.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".