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Record W4403144810 · doi:10.1111/apa.17445

Systematic review of environmental noise in neonatal intensive care units

2024· review· en· W4403144810 on OpenAlexaboutno aff
Liang Andy, Fan He, Sung Valerie

Bibliographic record

VenueActa Paediatrica · 2024
Typereview
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNeonatal intensive care unitNoise (video)Sound level meterIntensive careIntensive care unitMEDLINEEnvironmental noiseSound (geography)Noise levelAudiologyIntensive care medicinePediatricsComputer scienceAcousticsArtificial intelligence

Abstract

fetched live from OpenAlex

AIM: To systematically review the literature on noise exposure within the neonatal intensive care unit/special care nursery settings, specifically to describe: noise characteristics, sources of noise and ways of measuring noise. METHODS: Systematic searches were conducted through databases Medline, Embase and PubMed. Studies were included if they met the inclusion criteria (1) reported noise characteristics; (2) reported noise exposure measurements; (3) in the neonatal intensive care unit/ special care nursery settings. Methods and key findings were extracted from included studies. Quality analysis was done using a modified version of the Newcastle-Ottawa Scale. RESULTS: We identified 1651 studies, screened 871, reviewed 112 and included 47. All reported NICU average equivalent sound levels were consistently louder than recommended guidelines (45 dB). The most consistent association with higher sound pressure levels were noise sources grouped under people congregation. Half of the studies did not use measuring devices adhering to international sound level meter (SLM) standards. CONCLUSION: All NICUs exceeded recommended accumulative sound levels. People were the most consistent source of noise. Sound pressure levels need to be consistently measured with devices adhering to international SLM standards in future studies.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.279
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.280
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations11
Published2024
Admission routes1
Has abstractyes

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