The spatial and temporal effect of electrochromic windows on indoor and human microbiome in an inpatient hospital
Bibliographic record
Abstract
Abstract Objective: Improving the hospital environment and developing novel disinfection strategies are critical for infection control in healthcare settings. In this study, we explored the effects of electrochromic (EC) windows on indoor and patient microbiome in an inpatient hospital. Patient and setting: Hematology-Oncology patients at the University of Vermont Medical Center Methods: We conducted a prospective study in ten occupied patient rooms. Five of the patient rooms had active EC windows that tint dynamically to control for heat and glare, and the other five rooms had deactivated EC windows that simulated traditional windows and blinds. Samples were collected one day before patient admission as baseline and on the 1st, 3rd, and 5th day of the patient stay. Total bacterial abundance and bacterial community structure were determined through quantitative PCR and 16s rRNA Illumina MiSeq sequencing, respectively. Results: Patient rooms with active EC windows had significantly lower light intensity and temperature than traditional patient rooms with blinds. The absolute bacterial abundance and diversities on windows were significantly lower in rooms with EC windows and the bacterial composition changed after one day EC window activation. Compared to baseline, relative abundance of the Staphylococcus genus was significantly lower on EC window surface during the five-day experiment. In contrast, the air microbiome was more diverse in rooms with EC windows. Conclusion: Active electrochromic (EC) windows in patient rooms result in lower light intensity and temperature, reduced bacterial abundance and diversities on window surfaces, and a more diverse air microbiome, informing future healthcare design.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".