Study of Bioaerosols in Surgical Theaters and Intensive Care Units from a Public General Hospital
Bibliographic record
Abstract
The health of building occupants may be affected by bioaerosols, particularly aerosolized bacteria and fungi. We determined airborne bacteria and fungi in 31 settings from a public general hospital. Air samples were taken by the impaction method on solid surface. Microbial identification was run by standard microbiological techniques. Results were interpreted by the criteria from The Spanish Association of Hospital Engineering and Canadian Health and Wealth Department. Results showed microbial density values ranging from 1 UFC/m 3 to 222 UFC/m 3 . In general, bacteria in surgical and non-surgical settings were within the ¨clean¨ range. However, the Oto-rhino M2 surgery room, D surgery room and the Nephrology surgery room exceeded the non-contaminated range. The Sterilization room and the Neonatal intensive care unit also depicted bacteria and fungi contamination, respectively. Coagulase-positive Staphylococcus was found in the Traumatology J surgery room and in the Urology S1 surgery room, while Serratia marcescens was isolated in the Nephrology surgery room. Therefore, J and S1 surgery rooms were also considered contaminated regardless of their low bacterial count. Bacterial identification revealed 14 genera and 8 species, being coagulase-negative Staphylococcus the most frequent bacterial isolate. The majority of the locations showed fungal densities values within the ¨very clean¨ and ¨clean¨ ranges, showing the isolation of 12 genera and 5 species. Aspegillus and Penicillum spp. were the most frequent fungal isolates. The indoor air microbiological quality in white theaters was determined by a rapid cultured-based method and a combination of indoor air microbiological quality criteria.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".