Impact of Electronic Faucets and Water Quality on the Occurrence of<i>Pseudomonas aeruginosa</i>in Water: A Multi-Hospital Study
Bibliographic record
Abstract
OBJECTIVE: To compare Pseudomonas aeruginosa prevalence in electronic and manual faucets and assess the influence of connecting pipes and water quality. SETTING: Faucets in 4 healthcare centers in Quebec, Canada. METHODS: Water samples from 105 electronic, 90 manual, and 14 foot-operated faucets were analyzed for P. aeruginosa by culture and enzymatic detection, and swab samples from drains and aerators were analyzed by culture. Copper and residual chlorine concentrations, temperature, and flow rate were measured. P. aeruginosa concentrations were analyzed in 4 consecutive volumes of cold water and a laboratory study was conducted on copper pipes and flexible hoses. RESULTS: P. aeruginosa contamination was found in drains more frequently (51%) than in aerators (1%) or water (culture: 4%, enzyme detection: 16%). Prevalence in water samples was comparable between manual (14%) and 2 types of electronic faucets (16%) while higher for foot-operated faucets (29%). However, type 2 electronic faucets were more often contaminated (31%) than type 1 (14%), suggesting that faucet architecture and mitigated volume (30 mL vs 10 mL) influence P. aeruginosa growth. Concentrations were 100 times higher in the first 250 mL than after flushing. Flexible hoses were more favorable to P. aeruginosa growth than copper and a temperature of 40°C led to higher counts. CONCLUSIONS: The types of faucets and connecting pipes, flow rate, and water quality are important parameters influencing the prevalence and the concentrations of P. aeruginosa in faucets. High concentrations of P. aeruginosa in the first 250 mL suggest increased risk of exposure when using the first flush.
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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 | 0.002 | 0.001 |
| 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".