Contribution of the Intensive Care Unit Environment to Sleep Disruption in Mechanically Ventilated Patients and Healthy Subjects
Bibliographic record
Abstract
Recent studies have challenged the traditional hypothesis that excessive environmental noise is central to the etiology of sleep disruption in the intensive care unit (ICU). We characterized potentially disruptive ICU noise stimuli and patient-care activities and determined their relative contributions to sleep disruption. Furthermore, we studied the effect of noise in isolation by placing healthy subjects in the ICU in both normal and noise-reduced locations. Seven mechanically ventilated patients and six healthy subjects were studied by continuous 24-hour polysomnography with time-synchronized environmental monitoring. Sound elevations occurred 36.5 +/- 20.1 times per hour of sleep and were responsible for 20.9 +/- 11.3% of total arousals and awakenings. Patient-care activities occurred 7.8 +/- 4.2 times per hour of sleep and were responsible for 7.1 +/- 4.4% of total arousals and awakenings. Healthy subjects slept relatively well in the typically loud ICU environment and experienced a quantitative, but not qualitative, improvement in sleep in a noise-reduced, single-patient ICU room. Our data indicate that noise and patient-care activities account for less than 30% of arousals and awakenings and suggest that other elements of the critically ill patient's environment or treatment should be investigated in the pathogenesis of ICU sleep disruption.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".