Monitoring, control and reduction of bioaerosols in the intensive care unit in Turkey
Bibliographic record
Abstract
Maintaining high indoor air quality in hospitals is important for protecting the health of both personnel and patients. In this study, the results of air quality monitoring carried out in the intensive care unit of a hospital in Bolu, Turkey, are discussed. Air disinfection devices were used to reduce the concentrations of bacteria. A 4-week sampling programme was implemented. In the first and the last weeks of the sampling, the disinfection devices were turned off, while in the second and third weeks, they were turned on. Bioaerosols were collected, and their indoor levels were determined by counting all plate colonies and bacterial types were identified by matrix-assisted laser desorption/ionisation–time of flight mass spectrometry. The highest levels of bacteria were measured in the mornings and before the air disinfection devices were turned on. Doctors did their rounds at approximately nine to ten o’clock. The dominant bacteria in the air samples examined were Staphylococcus hominis, Micrococcus luteus, Staphylococcus capitis, Staphylococcus epidermidis, Corynebacterium afermentans and Staphylococcus haemolyticus. The results showed that the air disinfection devices were effective in decreasing the concentration of microorganisms between 83.4 and 55% in the intensive care unit, which is very important for the healthcare staff and patients, particularly during pandemics.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".