Sound reduction management in the neonatal intensive care unit for preterm or very low birth weight infants
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.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".