Mapping sources of noise in an intensive care unit
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Excessive noise in hospitals adversely affects patients' sleep and recovery, causes stress and fatigue in staff and hampers communication. The World Health Organization suggests sound levels should be limited to 35 decibels. This is probably unachievable in intensive care units, but some reduction from current levels should be possible. A preliminary step would be to identify principal sources of noise. As part of a larger project investigating techniques to reduce environmental noise, we installed a microphone array system in one with four beds in an adult general intensive care unit. This continuously measured locations and sound pressure levels of noise sources. This report summarises results recorded over one year. Data were collected between 7 April 2017 and 16 April 2018 inclusive. Data for a whole day were available for 248 days. The sound location system revealed that the majority of loud sounds originated from extremely limited areas, very close to patients' ears. This proximity maximises the adverse effects of high environmental noise levels for patients. Some of this was likely to be appropriate communication between the patient, their caring staff and visitors. However, a significant proportion of loud sounds may originate from equipment alarms which are sited at the bedside. A redesign of the intensive care unit environment to move alarm sounds away from the bed-side might significantly reduce the environmental noise burden to patients.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it