Assessing the impact of creating virtual windows on the incidence of delirium in a surgical intensive care unit: a before and after study
Bibliographic record
Abstract
Introduction: Delirium is a frequent and important problem in the intensive care unit (ICU), and non-pharmacological means of prevention are limited. The importance of the physical environment in the occurrence of delirium in intensive care has been reported, particularly the presence of windows and daylight. We organized a trial to evaluate if the installation of virtual windows in the form of paintings in rooms without an actual window can limit the occurrence of delirium in ICU patients. Methods: We conducted a retrospective pre and post cohort study in a surgical ICU of a university-affiliated hospital. Patients residing for more than 48 hours in a windowless room before and after the installation of virtual windows were included in the trial. The primary endpoint was the incidence of a positive screening test for delirium during their time in the ICU. The Intensive Care Delirium Screening Checklist (ICDSC) was used as an objective screening tool to assess the occurrence of delirium. Results: A total of 400 patients were included in this trial (pre group: n = 200; post group: n = 200). The groups were well balanced except for the score APACHE II who was significantly higher in the post intervention group. The incidence of a positive screening test for delirium was similar in both groups after correction for confounding factors (29% vs 27%; OR 0,906 [0,584-1,402], p=0,656). Conclusion: The installation of virtual windows did not reduce the incidence of delirium in a surgical intensive care unit. Keywords Critical care, delirium, windows, prevention, circadian cycle.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".