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Record W2084238515 · doi:10.1542/peds.2013-3617

Infant Sleep Machines and Hazardous Sound Pressure Levels

2014· article· en· W2084238515 on OpenAlexaff
Sarah C. Hugh, Nikolaus E. Wolter, Evan J. Propst, Karen A. Gordon, Sharon L. Cushing, Blake C. Papsin

Bibliographic record

VenuePEDIATRICS · 2014
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineAudiologyNoise (video)Hearing lossSound pressureSleep (system call)Sound (geography)AcousticsAmbient noise levelComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVE: Infant "sleep machines" (ISMs) produce ambient noise or noise to mask other sounds in an infant's room with the goal of increasing uninterrupted sleep. We suggest that the consistent use of these devices raises concerns for increasing an infant's risk of noise-induced hearing loss. We therefore sought to determine the maximum output levels of these sleep machines. METHODS: Sound levels of 14 ISMs played at maximum volume were measured at 30, 100, and 200 cm from the machine using correction factors to account for a 6-month-old's ear canal. RESULTS: Maximum sound levels at 30 cm were >50 A-weighted dB for all devices, which is the current recommended noise limit for infants in hospital nurseries. Three machines produced output levels >85 A-weighted dB, which, if played at these levels for >8 hours, exceeds current occupational limits for accumulated noise exposure in adults and risks noise-induced hearing loss. CONCLUSIONS: ISMs are capable of producing output sound pressure levels that may be damaging to infant hearing and auditory development. We outline recommendations for safer operation of these machines.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.248
Teacher spread0.236 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations23
Published2014
Admission routes1
Has abstractyes

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