Sound reduction management in the neonatal intensive care unit for preterm or very low birth weight infants
Why this work is in the frame
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Bibliographic record
Abstract
Infants in the neonatal intensive care unit (NICU) are subjected to stress, including sound of high intensity. The sound environment in the NICU is louder than most home or office environments and contains disturbing noises of short duration and at irregular intervals. There are competing auditory signals that frequently challenge preterm infants, staff and parents. The sound levels in NICUs often exceed the maximum acceptable level of 45 decibels (dB), recommended by the American Academy of Pediatrics. Hearing impairment is diagnosed in 2% to 10% of preterm infants versus 0.1% of the general paediatric population. Noise may cause apnoea, hypoxaemia, alternation in oxygen saturation, and increased oxygen consumption secondary to elevated heart and respiratory rates and may, therefore, decrease the amount of calories available for growth. Elevated levels of speech are needed to overcome the noisy environment in the NICU, thereby increasing the negative impacts on staff, newborns, and their families. High noise levels are associated with an increased rate of errors and accidents, leading to decreased performance among staff. The aim of interventions included in this review is to reduce sound levels to 45 dB or less. This can be achieved by lowering the sound levels in an entire unit, treating the infant in a section of a NICU, in a 'private' room, or in incubators in which the sound levels are controlled, or reducing the sound levels that reaches the individual infant by using earmuffs or earplugs. By lowering the sound levels that reach the neonate, the resulting stress on the cardiovascular, respiratory, neurological, and endocrine systems can be diminished, thereby promoting growth and reducing adverse neonatal outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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