Effect of music therapy on behavioral and physiological neonatal outcomes: A systematic review and dose-response meta-analysis
Bibliographic record
Abstract
BACKGROUND: Previous studies have documented the effectiveness of music therapy in improving adverse neonatal outcomes in premature infants. However, this review aims to address the question of how long listening to music can enhance these neonatal outcomes. METHODS: To conduct this dose-response meta-analysis, we searched the PubMed, Scopus, Web of Science, and Cochrane Library databases. The inclusion criteria comprised randomized clinical trials that investigated the effects of music therapy on improving adverse neonatal outcomes. Preterm infants were defined as those born between 27 and 37 weeks of gestation, as fetuses are known to respond to auditory stimuli starting at the 27th week of pregnancy. The initial search was performed on January 28, 2024, and there were no restrictions on the time frame for the search. Ultimately, we employed a two-stage random effects model using the "drmeta" package in Stata software to perform this dose-response meta-analysis. RESULTS: In total, 30 articles (1855 participants) were identified for inclusion in our meta-analysis. According to pooled analysis with each minute increase in music therapy, the means of respiratory rate, pain score, SBP, DBP, behavioral score, and body temperature decrease by 35.3 beats per minutes, 15.3 VAS, 30.7 mmHg, 8.9 mmHg, 2.7, and 0.27°C. On the other hand, with each minute increase in listening to the music, the mean of O2 saturation, heart rate and sleep duration increase 1.7%, 89.2 beats per minutes and 5.081 minutes per day, respectively. CONCLUSION: Music therapy improves the neonatal outcomes of O2 saturation, heart rate, respiratory rate, sleep duration, body temperature and systolic and diastolic blood pressures. Therefore, the existence of a dose-response relationship can indicate a causal relationship between music therapy and the improvement of 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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.048 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".