Infants born preterm, stress, and neurodevelopment in the neonatal intensive care unit: might music have an impact?
Bibliographic record
Abstract
AIM: The neonatal intensive care unit (NICU) provides life-saving medical care for an increasing number of newborn infants each year. NICU care, while lifesaving, does have attendant consequences which can include repeated activation of the stress response and reduced maternal interaction, with possible negative long-term impacts on brain development. Here we present a neuroscientific framework for considering the impact of music on neurodevelopment in the NICU of infants born preterm and evaluate current literature on the use of music with this population to determine what is most reliably known of the physiological effects of music interventions. METHOD: Using online academic databases we collected relevant, experimental studies aimed at determining effects of music listening in infants in the NICU. These articles were evaluated for methodological rigor, ranking the 10 most experimentally stringent as a representative sample. RESULTS: The selected literature seems to indicate that effects are present on the cardio-pulmonary system and behavior of neonates, although the relative effect size remains unclear. INTERPRETATION: These findings indicate a need for more standardized longitudinal studies aimed at determining not only whether NICU music exposure has beneficial effects on the cardio-pulmonary system, but also on the hypothalamic-pituitary-adrenal axis, brain structures, and cognitive behavioral status of these children as well. WHAT THIS PAPER ADDS: Provides a neuroscience framework for considering how music might attenuate stress in neonatal intensive care unit (NICU) infants. Considers how repeated stress may cause negative neurodevelopmental impacts in infants born preterm. Posits epigenetics can serve as a mechanistic pathway for music moderating the stress response.
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.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".