Legionella and agar cultures in healthcare facilities waters: a monitoring study in North-East Italy
Bibliographic record
Abstract
Abstract Background Water healthiness is fundamental in medical facilities. Monitoring Legionella spp. and agar cultures concentration is therefore very important. This study looks into these parameters to examine possible criticalities that could need interventions. Methods We collected 399 Legionella and 82 agar cultures withdrawals between November 2019 and February 2020 from 157 detection points (like taps and showers) in 31 structures of four health districts. Withdrawals referred to the 4 quarters of year 2019 (t1, t2, t3, t4). Legionella values were expressed in colony forming units (CFU)/l; agar cultures in CFU/ml. We performed a non-parametric analysis to compare mean values between districts. We used the statistical software package SAS version 9.4 for Windows, setting the significance level at α = 0.05. Results 45% of structures presented at least one positive withdrawal for Legionella (in six we found positivity in more than 1 quarter). Concerning Legionella concentration, we found significant difference between districts in t2 (p = 0.012) and between wards of one district in t3 (p < 0.05). We observed agar positive values in 11 structures (in four cases in more than 1 quarter). In t4 we found a very significant difference (p < 0.0001) between agar values in the four districts, while in t3 we observed a non significant association (p = 0.067). In t3 we found a significant difference for agar values between wards of one district (p = 0.025). We finally considered the simultaneous presence or absence of Legionella and agar positivities: without considering quarters division, we observed accordance (both present or both absent) in 60 detection points, while in 34 points we didn't find it. Conclusions Data provided by this study show that Legionella is rather present in our districts, revealing an association with concentration of CFU in agar. Disinfecting operations should be implemented considering the differences between districts to provide a safe water in every ward. Key messages Water quality monitoring in healthcare facilities is fundamental to provide a safe and healthy environment. Legionella spp. and agar cultures concentrations depend by quarter and spot considered; focused operations should be taken into account to improve disinfection quality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".